knowledge synthesis of recommendation systems finding expert recommendations for cuisines

INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 3, ISSUE 05 13 ISSN 2347-4289

Copyright 2015 IJTEEE.

Knowledge Synthesis Of Recommendation Systems - Finding Expert Recommendations For Cuisines. Minakshi Gujral, Satish Chandra (Assistant Professor) Department of Computer Science and Engineering, JIIT, Noida, India; (Assistant Professor) Department of Computer Science and Engineering, JIIT, Noida, India. Email: [email protected], [email protected] ABSTRACT: Knowledge discovery tools and techniques are used in an increasing number of scientific and commercial areas. They further augment the analysis and knowledge processing of voluminous Information. Expert finding systems are a web enabled Knowledge Discovery from databases frameworks. Lot has been talked about effective, accurate and well balanced expert recommendations but many shortcomings of the proposed solutions have come into picture. In this Paper we try to elucidate and model Expert Recommendation issues from multidimensional, multi-criteria and real worlds perspectives by evaluating some selected systems. Another aspect of expert recommendation prototype introduced in this paper is personalization. Due to information overload and other issues of recommendations, an internet user feels it difficult to search the expert information relevant to them. Local search is yet another field that faces this problem due to unavailability of expert, maturation effect of environment and changing patterns of user likings and Interest. This work portrays a personalized expert recommendation system which takes into account the profile attributes of a user and recommends results that are highly rated by other users of similar profile. The introduced method does not depend only on the ratings given by the user as a feedback but it also considers various other parameters which increase accuracy of recommendations. This prevents malicious results to be highly rated and recommended. Keywords : Expert finding systems; Knowledge engineering; Recommendation systems; Information Systems, Personalized ratings; Information Over-load; ratings; personalized recommendations; Expert locating systems.

1 INTRODUCTION THE Expertise recommendation systems are the new search paradigm which has stormed the E-world with semantic prefe-rences and excellent information services. Some of the re-nowned expertise recommendation systems are Amazon, last.fm, Netflix, Cinematch, yahoo and Google. Expertise rec-ommendation Systems lays strong foundation for the commer-cially rich and ever expanding expertise recommendation cul-ture with ever growing online commerce. Not only it proves solution for Information Overload problems but also provides users with novel and preferred product, concepts and services relevant to their preferences. The Social aspect of expertise recommendations shapes the cultural flow. Here we analyze some selected but generic expertise recommendation systems of various application environments and different expertise recommendation methods, their performances, user and item profile aspects, rating structures, similarity measures, and oth-er issues. This gives a Multi dimensional evaluation framework to model optimized system for best fit expertise recommenda-tions given by Matrix 1.1 in Fig. 1.The research for optimized expertise recommendations started from way back mid 1990 to this current era, but no concrete, balanced and feasible so-lution from multidimensional perspective has been given till date. This analysis can be extended with no of issues and sys-tems. There are viable paths of improvements and extensions, which can be implemented, mixed and matched for feasible, environmentally tailor made best fit expertise recommenda-tions. The expertise recommendation Quality has multilingual aspects to it while diving deep in research, innovation and novel ideas for expertise recommendations. It is the search for best fit solution for any application, technology and for varying demography of users. We need to explore Multi dimensional issues and parameters to produce best fit and optimized ex-pertise recommendations. Expertise Recommendations pro-duced by the system will depend on the profile of the user and ratings which the businesses have received. For example if a

user is a kid of around 15 years and is searching for computer books he will be recommended book stores selling basic com-puter books (like those teaching how to use MS Office or using HTML) in his/her nearby location. But if the user is a professor in Computer Science then for the same search query he will be recommended book stores selling sophisticated books on various computer subjects like networking, database or artifi-cial intelligence that are in proximity to the user location. The existing search systems in local domain give general results i.e. results will not depend on the profile of user. The novel, accurate and environment friendly system proposed by us will perform profile matching to find other users with similar profile and will check the ratings contributed by them. To promote accuracy the priority of recommendations is not just depen-dent on feedback from user but also on other factors which measures user likeness towards the business.

Fig. 1. The Issue Matrix: Recommendation Systems and their Issues [Matrix 1.1]



2 EVALUATING MULTI DIMENSIONAL EXPERTISE RECOMMENDATION FRAMEWORK. Expertise recommendation systems apply to multiple application domains but best fit recommendation evaluation is still a difficult task [1, 2]. After evaluating and analyzing Fig. 1 there are some critical points of evaluations which comes into picture are as fol-lows:

1. Similarity Measure cannot be implemented for all users of varying preferences.

2. There is a need to capture quality and usefulness of recommendations from the users satisfaction perspec-tives and other criterias like coverage, algorithmic complexity, scalability, novelty, confidence, user-friendliness and trust.

3. The need to design an efficient, user friendly user inter-face also hinders effective recommendation process. Most of the time the user gets confused and disinte-rested.

4. Traditional Recommendation Methods i.e. content, col-laborative and hybrid [depending on rating system], with their advantages and shortcomings contribute to-wards possible extensions. This capacitates them for large scale application domains like recommending va-cations, financial services.

5. The flipside of such systems is that although the Impro-vised Extensions are introduced but till date neither im-plemented nor explained concretely and explicitly in recommendation systems research [2].

6. These calculations further needs to be checked with ef-fective feedback of real world data rather than artificial datasets. These extensions should be further be ba-lanced with increase in information, user, network and addition of complex cross application networks.

Looking at Matrix 1.1 we find the semantic analysis of some se-lected systems and recommendation issues. Here a tick mark is put against system Sn which is having the the issue (Iy) where n and y can range from (1m), m being any positive number. The scale of tick mark can range from (0, 1/2,1) where 0 is no issue depicted by cross.This further can be extended with N number of systems and Y number of issues.

2.1 The Cold Start Problem The cold start problem occurs when a new user has not pro-vided any ratings yet or a new item has not yet received any rating from the users. The system lacks data to produce ap-propriate recommendations. [1][2].To remove over specializa-tion problem when we use diversification in explanations [11] we give chance to new items in the group which is good tes-tament to solution of cold start problem. CBR plus Ontology [12] concepts reasons out the current and old status of items as per logic based calculations rather than ratings. This in-creases the system complexity and even if it gives cold start solution to 15% it increases scalability sparsity and other is-sues. This 15% also is viable when information is well fed by past cases. Knowledge based Models[6] hits hard on usage of rating structure by Implicit recommendation methods[1,2] and proposes evaluating explicit user/item models to take care of cold start problem. The excellent analysis done by knowledge Model framework[16] which says that as new products of vari-ous categories arrive in market, this can further ignite cold start, over specialization and sparsity problems. Even clubbing Intelligent Agents and mining Semantic web [18] leads to cold start problem and with increase in scalability the system suf-fers. The Cold start problem is apparent in systems [12, 13, 18, 19, 20, 21, 22] depicted by Matrix 1.1.

2.2 The Coverage Problems in Recommendations While preferences can be used to improve recommendations, they suffer from certain drawbacks, the most important of these being their limited coverage. The coverage of a prefe-rence is directly related to the coverage of the attribute(s) to which it is applied. An attribute has a high coverage when it appears in many items and a low coverage when it appears in few items. [1][2]. Matrix 1.1 depicts the coverage problems in some systems [11, 12, 13, 15, 18, 19, 20, 21].

2.3 The Overspecialization in Recomemndations All Content-based approaches can also suffer from overspe-cialization. That is, they often recommend items with similar content to that of the items already considered, which can lead to a lack of originality. [5][6]. Moreover Matrix 1.1 depicts the Overspecialization problems in some systems [12, 14, 16, 19, 20, 21] As per Conversational strategy, which is best of above mentioned lot, all the preferences are to be specified upfront, at the beginning of the interaction only. This is a stringent re-quirement, because users might not have a clear idea of their preferences at that point. New Preferences and servicing needs to feed in, system itself cannot predict or recommend and again presents with old strategies and products [14]. Overspecialization is said to be solved effectively by attribute based diversity and explanation based diversity[11],of which explanation based diversity strategy reigns supreme as it takes care of computational cost, provides coverage for even social domain, and is better in efficiency and performance. But needlessly explanation is also a criterion added to content or collaborative based technologies, so it cannot escape from its structural disadvantages. Furthermore explanations need logic or reasoning to calculate satisfaction which is an inexplicable concept.

2.4 The User Satisfaction criteria of Recommendations. An important issue [1][2][3][5] in recommender system is to capture quality and usefulness of recommendations from the users satisfaction perspectives like coverage, algorithmic

Fig. 1. The Synthesis of Best Fit Recommendation Process



complexity, scalability, novelty, confidence and trust in user interaction. The need to design an efficient, user-friendly inter-face arises. For the implementation of Mixed hybrid approach, there is a decision involved i.e. which items should be rated to optimize the accuracy of collaborative filtering systems, and which item attributes are more critical for optimal content-based recommendations. This issue is worth exploring. Even if the recommender system is accurate in its predictions, it can suffer from the one-visit problem, if users become frustrated that they cannot express particular preferences or find that the system lacks flexibility. Creating a fun and enduring interaction experience is as essential as making good recommendations. Moreover Matrix 1.1 depicts this problem in some systems [12, 14, 15, 19].

2.5 Personalization Personalization is an important factor in recommendation process whether it is content based, collaborative, hybrid or demographic recommendation technique. Personalization gives one to one service to the end user. But with increase in volume of information, ever growing variety of information to choose from and varying recommendation techniques, com-plex information overload problem arises. Moreover, processing of user preferences is difficult due to following rea-sons:

1. Different User Background: User belonging to different groups or cluster differs in taste; also individual needs change with environment.

2. Registered/Unregistered Users i.e. whether or not the user is providing with true information about his identity and taste. The browsing pattern and shopping history are important in deciding recommendations. The inten-sity of seriousness, faith and consistency in elicited in-formation is difficult to measure.

3. The information need and growth change with matura-tion effect. The knowledge level of the product and consumer change with time. Other factors like peer-review, demographic profile, after use satisfaction of similar products also affect personalization. Some of the systems [13, 17] in Matrix 1.1 elucidate such prob-lems.

2.6 The Scalability Problems in Recommendations. Some of the systems for example in Fig 1., have a limitation of scalability as depicted by Matrix 1.1. By increasing load on the recommendation in terms of growing item, users, the system slows down effective process. This degrades system perfor-mance, efficiency and throughput of recommendation system. In using Explanation Facility to solve over specialization[13] and to bring in diversity in product choice[11],the recommen-dation quality improve but with increase in users, items and modules of system, the result is complexity and overhead which further breaks the performance. Same happens with other systems. Increase in load or scalability is real test of sys-tem potential and capacity. Research solutions on small scale or fewer loads are feasible from all perspective but with scala-bility of system it is a different scenario in itself.

2.7 Scrutability Criteria editing recommendations. There are Recommendation Frameworks [15, 16, 19, 20 ] which exhibit absence of scrutability criteria as per Matrix1.1. Scrutability is one of recommendation quality parameter which permits user to alter his query to tailor fit his recommenda-

tions. [13] The user is giving his feedback for the recommen-dations via scrutability. Many recommendation algorithms dont allow user feedback or scrutability. This can lead to dissatis-faction of user and further add on to other issues as well.

2.8 Sparsity Issue in Recommendations. There are Recommendation Frameworks [11, 12, 13, 15, 20, 21] with sparsity issues as shown by Matrix 1.1. The Concept of sparsity leads to a situation when enough transactional data is not there for any type of correlation or mapping of [item/user] data. Be it recommendation technique of recom-mendations using diversification [11],explanation facili-ty[13],tags[15] and others, most of them lack transactional, linking data to map or correlate user/item models. In other words calculating the distance between entities [user/item] becomes difficult.

2.9 The Central Processing Unit of Recommendation

Framework- Quality of Recommendations. The Quality of Recommendations is measured by some prima-ry components such as Novelty, flexibility, scrutability, transpa-rency, effectiveness, efficiency and persuasiveness. But pres-ence of issues depicted by MATRIX 1.1 fails the most tailor made recommendations also. So the need arises to evaluate the recommendation quality from Multi dimensional qualitative perspectives. The various learning techniques i.e. ontology, repertory grid etc of these algorithms are evolved to resolve recommendation issues. Recommendation strategies also encompasses evaluations grounded in mathematical logic such as Pearsons co-efficient, top N Model[1],[2], but they are algorithmically striking a balance among prediction, recom-mendation ,relevance, diversity of items and measuring of is-sues which is not viable. This requires feasible real world modeled framework. The quality of Recommendation is a Multi dimensional evaluation from all these perspectives. The quality evolution started from content, collaborative and hybrid tech-niques [1], [2].Ratings helped to calculate user satisfaction, Personalization [19] brought semantic understanding to it. Recommendation Quality research further saw similarity measure, preference elicitation, difference between search recommend and predict to ascertain the exact co-relation be-tween user characteristics and item attributes. Tagging facility [15] solved the understanding of item background to an extent and Explanation facility [11], [13] further augmented this ap-proach. The Concept of Search Algorithms, repertory grids [3] and Graph models [6, 9, 10] also participated to churn out op-timized best fit recommendations. Conversational, Context and Critiquing process brought a see saw change in recom-mendation quality research [14, 23, 17]. Even Case Base rea-soning, Ontology [12], Intelligent Agents [21] also contributed towards this direction. The innovation and feasible technique illuminated by knowledge based models is the best measure of all for generating consistent recommendations.

2.10 Evaluation of recommendation parameters from

qualitative and quantative aspects. This evaluation metrics further illuminates various recommen-dation methodologies, architecture and their implementation and processing strategies with two main goals:

1. Recommendation systems quality evaluations 2. Recommendation systems maintenance in real world. 3. The social and technical parameters of recommenda-

tions: We can look at recommendation system [3][4]



from social and technical evaluation perspective .For this the author has taken 5 recommender systems Grouplens, Fab, Referral web, PHOAKS AND SITE-CEER.

For this the evaluation metrics are categorically divided into three major parts of evaluations:

1. The Technical Design Space 2. The domain space and characteristics of items eva-

luated 3. The domain space and characteristics of the partici-

pants and the set of evaluations. The MATRIX 1 [25][26] has given a balanced structure to eva-luate and satisfy various recommendation parameters so this has:

1. Recommendation Quality Framework: Good Evaluation Matrix which enhances recommendation quality. It talks about the parameters of quality evaluations which can provide sound base to explain issues. For example: Recommenders Density of recommendations tells you about over all coverage or sparsity criterias.

2. It also evaluates the Cost structure therefore evaluating the remuneration of each approach and strategy.

3. Consumers Taste parameter evaluates the over specia-lization issue also to some extent can help to solve col-laborative filtering disadvantages due to inconsideration of different user background.

4. Recommendation systems implementation, usage and evaluation is a costly transaction. This has to be stra-tegically managed by structuring balanced quotient of multidimensional Recommendation quality criterias and Business Models needs. Cost and quality parame-ters should be managed according to application do-mains need and users perspective.

2.11 The Environment of Recommendation process. The basic context of recommendation must pass through the test of validation through comfort of end user, matching his knowledge context, availability of recommended resources and the comfort zone of users to give accurate feedback or ratings for further analysis. In this work, the recommendation parameters, environment and context is well balanced with end user, the nature of the query, the availability and newness of the recommendation and maturation effects of data, people and external factors. The recommendations should be processed for best fit environment according to end user ra-ther than what everyone of the same profile likes or what other recommendation systems top N results are. The touch of per-sonalization, Cost metrics, feasibility to process the recom-mendation is also acute factors to consider. MATRIX 1[25][26] Recommendation quality evaluation parameter matrix from social and technical centric view [25][26 ]

1. The Technical Design Space defined by 5 dimensions System: Grouplens, Fab, Referral web, PHOAKS AND SITECEER.

2. Contents of recommendation: contents of an evaluation can be anything from a single bit (recommended or not) to unstructured textual annotations.

3. Explicit entry: Recommendation can be mined implicit or explicit users, users browsing time, personal book-mark list, Usenet articles of users.

4. Anonymous: Recommendations may be anonymous or

tagged with some source or destination value. 5. Aggregation: GroupLens, PHOAKS, and Siteseer em-

ploy variants on weighted voting. Fab to combines evaluations with content analysis. Referral-Web com-bines suggested links between people to form longer referral chains. Finally, the (perhaps aggregated) eval-uations may be used in several ways: negative recom-mendations may be filtered out, the items may be sorted according to numeric evaluations, or evaluations may accompany items in a display.

6. Use of recommendations: Application Domain. The domain space and characteristics of items eva-luated 1. Type of items: ex. netnews, articles ,urls, people. 2. How many: ex. 1000/day basically the frequency of

recommendations. 3. Lifetime: ex: 1-2 week,2 yrs etc 4. Cost structure. The domain space and characteristics of the partici-pants and the set of evaluations 1. Recommenders Density of recommendations : overall

coverage criteria 2. Consumers type: implicit or explicit users or particular

user group. 3. Consumers Taste: the type of like minded users or dif-

ferent background users 4. Consumers Taste variability: How is difference in

choice and preference calculated. The other aspect of Social centric recommendation [3][4] eval-uation is pointing towards the problems arising because of multiplicity of information and inaccurate navigation leading to complex navigation, overspecialization and other recommen-dation issues. Casestudy 1 MEDLINE, the online archive of published articles in medicine, grows at a rate of almost 500,000 entries per year. Much of this research and many of the deployments have focused on the algorithms that underlie the recommender systems. Algorithmic research has been rich and successful, exploring dozens of linear algebra, statistical, and machine learning approaches to producing ever more accurate recommendations. Algorithmic field also meets with limitations. If we try to find a way of those limitations in new algorithms, it offers decreasing marginal return on the creative, intellectual, and experimental resources they require [3] [4] Findings: The ideal recommendation system is the one which search, browse, and understand the results of the recommen-dations and provide that through easy and understandable navigation links. To further sustain this viewpoint there are two examples quoted: Casestudy 2 Designing and Evaluating Ka-las: a Social Navigation System for Food Recipes presents a complete overview of Kalas, a social navigation system for recipes, including a longitudinal user study. Social navigation systems make visible to users the pathways other users have taken through an information space with the thought that future users can learn from what past users have done.[3][4] Find-ings: Implicit and explicit user navigation problems are impor-tant to resolve recommendation problems. A good navigation, excellent user interface are benchmarks of best fit recommen-dation giving system. But the factors of novelty of information, usability of information according to context of users from dif-ferent domain have to be taken into account while resolving



navigation links. Casestudy 3 The article, Social Matching: A Framework and Research Agenda analyzes the problem of social matching which attempts to connect people to each oth-er based on common characteristics. This presents taxonomy of social matching systems and shows how many of the pre-viously studied systems fit into the taxonomy. [3][4] Findings: This system gives excellent user and system interface har-nessing the recommendation technologys potential in broader design space. It provides dynamic interaction and navigation. There are some questions pointed on this analysis:

1. The Reliability of this system can be at stake. Thispoint comes when fake ratings are done in shiiling attack process. It is difficult to ascertain trust issues in such recommendations.

2. There is a problem og gaining space in new market by New User and New Item problem. They both suffer heavy cold start and overspecialization problem. People tend to go towards already recommended and famous brands.

3. The coverage of context of user, data and environment is also important parameter hindering best fit recom-mendations.

4. Lastly Recommendation systems research is much more than user interface and navigation problems. The criterias of coverage, sparsity, and other issues are not balanced in this approach.

Recommendation System can be termed as Information Filter-ing KDD Technique which evolves with E-world. It is a way ahead of smart search. We categorize recommender systems semantically on large scale. This includes applications ranging from e-commerce to social networking, platforms from web to mobile, healthcare to differential diagnosis, project manage-ment to quality assurance and beyond, and a wide variety of technologies ranging from collaborative filtering, content based, hybrid approaches, demographic Filtering to rule based. In this work the recommendation systems are study systems and analyzed on basis of some Issues. The main goal is trust worthy, user friendly, conversational, context enriched, novel best fit recommendations. In order to balance data, people and environment with maturation effect, we should look at recommendations from three dimensional environments.

3 THE PROPOSED ARCHITECTURE FOR BEST FIT EXPERTISE RECOMMENDATIONS IN CUISINES. As After the study of recommendations and expert findings of some authentic, real world implementations [24-38], this paper presents a novel architecture for best fit expertise recommen-dations in Cuisines. The systems take demographic characte-ristics of a user like age, income level, location etc. to map his profile. This data is stored and processed while registering the user. When user search for a product or service the system first search for E-Business offerings that product or service can offer and then finds other users who have rated these found out businesses. The recommendations produced will be displayed in decreasing order of their ratings. The ratings allo-cated to each business will depend on the following parame-ters:

1. Feedback from user. 2. Number of times item is clicked in list of recommenda-

tions. 3. Number of times the item was first clicked in recom-

mendation list.

4. Number of clicks done on item businesss web page. 5. Number of times E-business generated. 6. For how much time the user remained on the business

web page. The businesses are sorted and classified according to their ratings. The businesses receiving high ratings are given A+ or A grade and are highly recommended. The lower the ratings, the lower the chance a business is recommended. Further low ratings businesses are recommended lower in the list. A busi-ness getting low recommendation grade for a user may get a high recommendation grade for another user as it may be suit-ing that user more. The business getting C or D will be either low rated or new businesses. In case of cold start these results will be recommended. Further lower rated businesses are considered malicious and are avoided from being recom-mended..

4 THE PROPOSED ALGORITHM FOR BEST FIT EXPERTISE RECOMMENDATIONS IN CUISINES. Expertise Recommender System aims to prioritize items ac-cording to users interest by taking care of his location. User will enter expert item he want to search. Recommender (Re-source/Business, item_to_be_searched) Step 1: Retrieve user information (age, income etc.) from his profile Extract and Tag his profile as Topical/Social/Implicit/Explicit. Step 2: In all registered businesses find the searched item Step 3: if (item is found in Step 2) Retrieve those businesses matches to user profile and priorit-ize them in order of their grade Step 4: Calculate_Rating() Step 5: Deciding_Ratings_Grades(registered businesses) Step 6: if (rating_grade==A or B) Most preferable else if (rating_grade==C) New or Less preferable else malicious entries Send the calculated rated items for further check in Issue Syn-thesis Grid depicted by Fig. 1. Mark them with heuristics as per given below: 9 Flags for each issue, rate with -1 such as for issue flag= 1 do +1 and for issue flag=0 do 1,store Total_Issue_Score. Step 7: Recommend businesses in decreasing order of their ratings. If we take ratings simply based on users feedback might not be a good approach because lot of malicious entries can be theirs. Here our proposed system will generate its own rating matrix based on various parameters. Calculate _Rating () Step 1: Define Resource Rating table (nxm matrix) for each kind of resource, n represents different categories of users and m represents various parameters which can affect rating of that particular resource. Various parameters include: Session_Time ()



Start timer on page load Stop timer on page exit Calculate elapsed time (stop-start) aking average with previous elapsed times and update it in rating table Hit_Counter () Set global application variable count Count ++ on every page load E-business Generated () User feedback Link Clicks etc... Step 2: Associate weight for these parameters. Pi=parameters Wi= (w1, w2, w3, etc...) Rating=Pi*Wi Such that Wi=1 Deciding Rating Grades (Aray_of_Businesses_in_which_Item_was_Found) Step 1: Sort businesses with ratings in decreasing order Step 2: r=total no. of resources j=0; for (i=0 to r/7) rating_grade [j++] =A+; for (i=r/7 to 2* r/7) rating_grade [j++] =A; .. After this module Check with Total_Issue_Score and Show them as seprate tags for rating structure. Present both solu-tions with and without Issue Tagging. System should perceive recommendations which might be useful to user even if he doesnt intentionally or explicitly asks for it. Step 1: Store browsing history for each resource. Step 2: Maintain separate knowledge base for that purpose. Step 3: When user search item of particular domain recom-mend him other items which were searched by other users in the same domain Recommended. This algorithm is subjected to maturation effect and sacalability parameters in environ-ment. But this can be controlled by generating data via craw-lers and filtering them according to context of end users need.

5 IMPLEMENTATION AND DISCUSSION OF PROPOSED EXPERTI CUISINE RECOMMENDING ALGORITHM We have demonstrated our algorithm by taking example of restaurant domain Suppose user search chowmein in category Chinese Restaurant and we have to recommend restaurants by taking care of various parameters like user profile, location, ratings of restaurants. Recommender (Chinese Restaurant, Chowmein) Step 1: Retreive user information including his age, income, location etc. Extract and Tag his profile as Topical/Social/Implicit/Explicit. Step 2: In all registered Chinese Resturants find Chowmein Step 3: if (chowmein is found in descriptions of chinese restau-rants) Retreive those chinese restuarants matches to user profile and prioritize them in order of their grade Step 4: Calculate_Rating() Step 5: Deciding_Ratings_Grades(Chinese Restaurants) Step 6: if (rating_grade==A or B) Most preferable chinese restuarants. else if (rating_grade==C) New or Less preferable chinese restaurants

else malicious entries Send the calculated rated items for further check in Issue Syn-thesis Grid depicted by Fig. 1. Mark them with heuristics as per given below: 9 Flags for each issue, rate with -1 such as for issue flag= 1 do +1 and for issue flag=0 do 1,store Total_Issue_Score. Step 7: Recommend chinese restaurants in decreasing order of their ratings. Calculate Rating () Step 1: Decided on the bases of various parameters like Session_Time () Start timer on page load Stop timer on page exit Calculate elapsed time (stop-start) Taking average with previous elapsed times and update it in rating table Hit_Counter () Set global application variable count Count ++ on every page load E-business Generated () User feedback Link Clicks etc Step 2: Calculate total rating Pi=parameters Wi=weight assigned to these parameters Rating=Pi*Wi Such that Wi=1 Deciding Rating_Grades (Chinese_restaurants cr []) Step 1: Sort these ratings in decreasing order Step 2: n=total no. of restaurants j=0; for(i=0 to n/7) rating_grade [j++] =A+; for (i=n/7 to 2* n/7) rating_grade [j++] =A; ................ After this module check with Total_Issue_Score and Show them as seprate tags for rating structure. Present both solu-tions with and without Issue Tagging. Suppose manchurian, momos, noodles etc. are other keywords searched by different users for the same domain Chinese restaurants. We maintain separate knowledge base for that and when user search chowmein in the domain Chinese restaurant then these key-words along with Chinese Restaurants will be recommended to user. Also by second extended solution of showing tags with issues explains the user, the strength and weakness of the decided option. This should help the user to understand his requirement and give authentic feedback after real time expe-rimentation.

6 CONCLUSION An Expert Finding system or Expert decision Recommenda-tion System can be termed as Information Filtering, Know-ledge Discovery Technique which evolves with E-world. We categorize recommender systems semantically on large scale. This includes applications ranging from e-commerce to social networking, platforms from web to mobile, healthcare to diffe-rential diagnosis, project management to quality assurance and beyond. And also this parses a wide variety of technolo-gies ranging from collaborative filtering, content based, hybrid



approaches, explanations facility, tags terminology, ontology, case-based reasoning to Knowledge Models. Thus a need arises to evaluate and explore recommendation architecture with perspective of issues and quality parameters. Further Recommendation systems need more cross dimensional analysis from the perspective of Issues, Qualitative and Quan-tative Framework, Business Model, Architecture and Applica-tion Domains. This paper has taken the example of Chinese cuisine and tried to solve this multi dimensional issue matrix given in Fig. 1. By the proposed approach implemented on Fig. 2. Our proposed Expertise Recommendation system algo-rithm can solve the problem of information overload from perspective of personalization and complexity criteria. This work will provide user with concise, contextual and real time information. This proposed project will provide useful recom-mendations to the user that will match with his profile (inter-ests) and will be worth recommending (having high rating val-ue). The system success can be analyzed by the satisfaction levels of various users. The main focus of this paper has main-ly been discussion of Expertise Recommendation issues and focussing best fit recommendations in Cuisines. There are many Issues and some of them are overlapping as well. The accurate and best fit recommendation generating evaluation framework is future work. There is lot more to be done and more importantly issues have to be clearly and broadly classi-fied.

ACKNOWLEDGMENT The authors wish to thank Mr Abhishek Begaria and Mr Kunal Kapoor for making of this Architecture and implementation of the Code. Their hard work and enthusiasm is praiseworthy.

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knowledge synthesis of recommendation systems finding expert recommendations for cuisines

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