Proceedings ArticleDOI
Using location for personalized POI recommendations in mobile environments
Tzvetan T. Horozov,Nitya Narasimhan,Venugopal Vasudevan +2 more
- pp 124-129
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TLDR
This paper proposes an enhanced collaborative filtering solution that uses location as a key criterion for generating recommendations, and describes preliminary results that indicate the utility of such an approach.Abstract:
Internet-based recommender systems have traditionally employed collaborative filtering techniques to deliver relevant "digital" results to users. In the mobile Internet however, recommendations typically involve "physical" entities (e.g., restaurants), requiring additional user effort for fulfillment. Thus, in addition to the inherent requirements of high scalability and low latency, we must also take into account a "convenience" metric in making recommendations. In this paper, we propose an enhanced collaborative filtering solution that uses location as a key criterion for generating recommendations. We frame the discussion in the context of our "restaurant recommender" system, and describe preliminary results that indicate the utility of such an approach. We conclude with a look at open issues in this space, and motivate a future discussion on the business impact and implications of mining the data in such systems.read more
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References
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Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Journal Article
Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering.
TL;DR: This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.
Journal ArticleDOI
Amazon.com recommendations: item-to-item collaborative filtering
TL;DR: Item-to-item collaborative filtering (ITF) as mentioned in this paper is a popular recommendation algorithm for e-commerce Web sites that scales independently of the number of customers and number of items in the product catalog.
Proceedings ArticleDOI
Social information filtering: algorithms for automating “word of mouth”
Upendra Shardanand,Pattie Maes +1 more
TL;DR: The implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists, and four different algorithms for making recommendations by using social information filtering were tested and compared.