zomato crawler & recommender

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  • CONTENT & LOCATION AWARE

    RESTAURANT RECOMMENDATIONS

    USING URBAN REVIEW NETWORKS

    PROJECT REPORT

    Submitted By

    Jayant Jaiswal, Roll No-12600112104, Regn No-121260110042

    Shoaib Khan, Roll No-12600112163, Regn No-121260110101

    Rohan Agarwal, Roll No-12600112143, Regn No-121260110081

    Under the Supervision of

    Asst. Prof. Partha BasuchowdhuriComputer Science & Engineering

    in partial fulfillment for the award of the degree

    of

    BACHELOR OF TECHNOLOGY

    In

    COMPUTER SCIENCE & ENGINEERING

    HERITAGE INSTITUTE OF TECHNOLOGY, KOLKATA

    MAULANA ABUL KALAM AZAD

    UNIVERSITY OF TECHNOLOGY

  • Acknowledgements

    We would take this opportunity to thank Dr. P. Chaudhuri, Principal, Heritage In-stitute of Technology for giving us the golden opportunity of working on this projectand providing us with all the necessary facilities and resources to work towards com-pletion.

    We are thankful to Asst. Prof. Partha Basuchowdhuri, our advisor and guide, forhis continuous support, advise and words of encouragement without which we couldhave not seen through the completion of this project. He is not just an advisor but apatient teacher who has always been there solving our doubts no matter how trivialand providing us with valuable insights which helped us in every way possible. Wealso owe our sincere gratitude to Dr. Subhashis Majumder, the Head of the Depart-ment, for his enriching discussions, novel ideas and valuable feedbacks.

    We would also like to thank our teachers, faculty members and laboratory assistantsat the Heritage Institute of Technology for playing a pivotal and decisive role duringthe development of the project. Last but not the least we thank all friends for theircooperation and encouragement.

    Jayant Jaiswal

    Shoaib Khan

    Rohan Agarwal

    i

  • HERITAGE INSTITUTE OF TECHNOLOGY

    MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY

    BONAFIDE CERTIFICATE

    Certified that this Project Report : CONTENT & LOCATION AWARERESTAURANT RECOMMENDATIONS USING URBAN REVIEW NET-WORKS is the bonafide work of Jayant Jaiswal, Shoaib Khan and RohanAgarwal who carried out this project work under my supervision.

    SIGNATURE SIGNATUREDr. Subhashis Majumder Asst. Prof. Partha BasuchowdhuriHead of the Department Project GuideComputer Science & Engineering Computer Science & EngineeringEast Kolkata Township, East Kolkata Township,Chowbaga Road,Anandapur, Chowbaga Road,Anandapur,West Bengal - 700107. West Bengal - 700107.

    SIGNATURE

    EXAMINER

    ii

  • Abstract

    Restaurant recommendation system is a very popular service whose so-phistication keeps increasing everyday.In this paper we present a per-sonalised restaurant recommendation system which has two parts toit. The first part recommends users restaurants based on their restau-rant review history. The second part recommends business owners withplaces perfect to open a restaurant with a particular cuisine where theowner would get the best traffic for the restaurant. Using Zomato data,we built a restaurant recommendation system for the individuals andbusiness owners. For each user in our data we find out the cuisinepreferences and other restrictions such as services offered, ambience,average rating, etc. and based on that we recommend the restaurantsaccordingly. We propose a metric that takes the popularity as well asthe sentiment of opinions for the food items based on the user gener-ated reviews as opposed to other systems where which only considerthe features mentioned above to recommend restaurants.

    iii

  • Contents

    1 Introduction 11.1 Road Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 What are Recommendation Systems? . . . . . . . . . . . . . . . . . . . . . . 1

    1.2.1 Collaborative Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.2 Content Based Filtering . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.3 Hybrid Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

    1.3 Motivation for Restaurant Recommendations . . . . . . . . . . . . . . . . . 3

    2 Literature Review 4

    3 Problem Definition 5

    4 Data Analysis 64.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64.2 Data Handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

    5 Methodology 75.1 Location Aware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75.2 Content Based . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

    6 Conclusion 12

    7 Future Works 13

    8 References 14

    iv

  • List of Figures

    5.1 Live Map of Kolkata sorted on the basis of ratings . . . . . . . . . . . . . . 75.2 The road network stored in PostgreSQL . . . . . . . . . . . . . . . . . . . . 85.3 Map of Kolkata showing the important intersections to setup a new restau-

    rant based upon a cuisine North Indian . . . . . . . . . . . . . . . . . . . . 95.4 The system taking user id as input to generate recommendations for that user. 115.5 Top 5 restaurants recommended by the system to the user for each food item 11

    v

  • Chapter 1

    Introduction

    1.1 Road Map

    In Chapter 1, we provide a broad description of the types of recommendation systemand applications of it in todays customer centric e-commerce market coupled with the basicknowledge about recommendation system. In Chapter 2 we give a brief overview of theprior works done in the field of restaurant recommendation. Chatpter 3 discusses aboutthe problem definition and terminologies related to it like content and location based rec-ommendation. In Chapter 4 we discuss the methods of fetching data and the preprocessingdone to suit the sytem and create good recommendation. Chapter 5 discusses about themethodologies and gives a detailed study about our system. The results of our systemon content and location specific recommendation are provided in Chapter 6. Scope forimprovements and future ideas are mentioned in Chapter 8 as future works.

    1.2 What are Recommendation Systems?

    Recommender systems have changed the way people find products, information, and evenother people. The goal of a Recommender System is to generate meaningful recommenda-tions to a collection of users for items or products that might interest them. It has changedthe way inanimate websites communicate with their users. Rather than providing a staticexperience in which users search for and potentially buy products, recommender systemsincrease interaction to provide a richer experience. The systems identify recommendationsautonomously for individual users based on past purchases and searches, and on other usersbehavior. They study patterns of behavior to know what someone will prefer from among acollection of things he has never experienced. The technology behind recommender systemshas evolved over the past 20 years into a rich collection of tools that enable the practitioneror researcher to develop effective recommenders.

    1.2.1 Collaborative Filtering

    Collaborative filtering methods are based on collecting and analyzing a large amountof information on users behaviors, activities or preferences and predicting what users willlike based on their similarity to other users. A key advantage of the collaborative filtering

    1

  • approach is that it does not rely on machine analyzable content and therefore it is capable ofaccurately recommending complex items such as movies without requiring an understandingof the item itself. Many algorithms have been used in measuring user similarity or itemsimilarity in recommender systems. For example, the k-nearest neighbor (k-NN) approachand the Pearson Correlation.

    1.2.2 Content Based Filtering

    Content-based filtering methods are based on a description of the item and a profile ofthe users preference. In a content-based recommendation system, keywords are used todescribe the items; beside, a user profile is built to indicate the type of item this user likes.In other words, these algorithms try to recommend items that are similar to those thata user liked in the past (or is examining in the present). In particular, various candidateitems are compared with items previously rated by the user and the best-matching itemsare recommended. This approach has its roots in information retrieval and informationfiltering research.

    1.2.3 Hybrid Approach

    Recent research has demonstrated that a hybrid approach, combining collaborative fil-tering and content-based filtering could be more effective in some cases. Hybrid approachescan be implemented in several ways, by making content-based and collaborative-based pre-dictions separately and then combining them, by adding content-based capabilities to acollaborative-based approach (and vice versa), or by unifying the approaches into one model.Several studies empirically compare the performance of the hybrid with the pure collabo-rative and content-based methods and demonstrate that the hybrid methods can providemore accurate recommendations than pure approaches. These methods can also be used toovercome some of the common problems in recommendation systems such as cold start andthe sparsity problem. Netflix is a good example of a hybrid system. They make recommen-dations by comparing the watching and searching habits of similar users (i.e. collaborativefiltering) as well as by offering movies that share characteristics with films that a user hasrated highly (content-based filtering).

    1.2.4 Applications

    1) Facebook users a recommender system to suggest Facebook users you may know offline.The system is trained on personal data