Contextual restaurant recommendation utilizing implicit feedback

Selecting a good, appropriate restaurant for an event is a common problem for most people. In addition to the main features of restaurants (e.g. food style, price, and taste), a good recommendation system should also consider diners' context information. Although there are many context-aware restaurant recommenders, most of them only focus on location information. This research aims to incorporate a greater variety of useful contexts into the recommendation process. Instead of explicit user restaurant ratings, our system relies on diners' restaurant booking logs to recommend restaurants. Each booking record contains the dining context: event type, dining time, number of diners, etc. In this paper, we propose using the canonical decomposition Bayesian personalized ranking (CD-BPR) algorithm to model the context information in a restaurant booking record. Experiments were conducted using three years of booking logs from EZTable, the largest online restaurant booking service in Taiwan. Experiment results show that adding context information into BPR significantly outperforms the baseline BPR method.

[1]  Qiang Yang,et al.  One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[2]  Jaime Teevan,et al.  Implicit feedback for inferring user preference: a bibliography , 2003, SIGF.

[3]  GeunSik Jo,et al.  Location-Based Service with Context Data for a Restaurant Recommendation , 2006, DEXA.

[4]  Sung-Bae Cho,et al.  Location-Based Recommendation System Using Bayesian User's Preference Model in Mobile Devices , 2007, UIC.

[5]  Kenta Oku,et al.  Context-Aware SVM for Context-Dependent Information Recommendation , 2006, 7th International Conference on Mobile Data Management (MDM'06).

[6]  Prabhakar Raghavan,et al.  Competitive recommendation systems , 2002, STOC '02.

[7]  Yoshiharu Ishikawa,et al.  Skyline queries based on user locations and preferences for making location-based recommendations , 2009, LBSN '09.

[8]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[9]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[10]  Yasuhiko Kitamura,et al.  A Competitive Information Recommendation System and Its Behavior , 2002, CIA.

[11]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[12]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[13]  Kenta Oku,et al.  A ranking method based on users' contexts for information recommendation , 2008, ICUIMC '08.

[14]  Alexander Tuzhilin,et al.  Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems , 2009, RecSys '09.

[15]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.