ABSTRACT
Recommender systems have become increasingly popular in recent years because of the broader popularity of many web-enabled electronic commerce applications. However, most recommender systems today are designed in the context of an offline setting. The online setting is, however, much more challenging because the existing methods do not work very effectively for very large-scale systems. In many applications, it is desirable to provide real-time recommendations in large-scale scenarios. The main problem in applying streaming algorithms for recommendations is that the in-core storage space for memory-resident operations is quite limited. In this paper, we present a probabilistic neighborhood-based algorithm for performing recommendations in real-time. We present experimental results, which show the effectiveness of our approach in comparison to state-of-the-art methods.
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Index Terms
- Recommendations For Streaming Data
Recommendations
Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data miningThe goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
Exploiting Linked Open Data in Cold-start Recommendations with Positive-only Feedback
CERI '16: Proceedings of the 4th Spanish Conference on Information RetrievalIn recommender systems, user preferences can be acquired either explicitly by means of ratings, or implicitly --e.g., by processing text reviews, and by mining item browsing and purchasing records. Most existing collaborative filtering approaches have ...
Scalable stream-based recommendations with random walks on incremental graph of sequential interactions with implicit feedback
AbstractRecommender systems are designed to recommend items to users based on their interests. Considering that in real-world scenarios user feedback is generated continuously at unpredictable rate, it becomes desirable to design models that learn from ...
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