Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Hybrid web recommender systems
The adaptive web
Modern Information Retrieval
Auralist: introducing serendipity into music recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
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Music is a highly subjective domain, which makes it a challenging research area for recommender systems. In this paper, we present our TRecS (Track Recommender System) prototype, a hybrid recommender that blends three different recommender techniques into one score. Since traceability is an important issue for the acceptance of recommender systems by users, we have implemented a detailed explanation feature that supports transparency about the contribution of each sub-recommender for the overall result. To avoid overspecialization, TRecS peppers the result list with recommendations that are based on a serendipity metric. This way, users can benefit from both recommendations aligned with their current taste while gaining some diversification.