Abstract
Recommender systems have been a valuable component in various online services such as e-commerce and entertainment. To provide an accurate top-N recommendation list of items for each target user, we have to answer a very basic question of how to model users’ feedback effectively. In this article, we focus on studying users’ explicit feedback, which is usually assumed to contain more preference information than the counterpart, i.e., implicit feedback. In particular, we follow two very recent transfer to rank algorithms by converting the original feedback to three different but related views of examinations, scores, and purchases, and then propose a novel solution called holistic transfer to rank (HoToR), which is able to address the uncertainty challenge and the inconvenience challenge in the existing works. More specifically, we take the rating scores as a weighting strategy to alleviate the uncertainty of the examinations, and we design a holistic one-stage solution to address the inconvenience of the two/three-stage training and prediction procedures in previous works. We then conduct extensive empirical studies in a direct comparison with the two closely related transfer learning algorithms and some very competitive factorization- and neighborhood-based methods on three public datasets and find that our HoToR performs significantly better than the other methods in terms of several ranking-oriented evaluation metrics.
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Index Terms
- Holistic Transfer to Rank for Top-N Recommendation
Recommendations
Top-N recommendation through belief propagation
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementThe top-n recommendation focuses on finding the top-n items that the target user is likely to purchase rather than predicting his/her ratings on individual items. In this paper, we propose a novel method that provides top-n recommendation by ...
Personalized hybrid recommendation for group of users
Novel group hybrid method combining collaborative and content-based recommendation.Proposed method improves the quality of recommended items ordering.Proposed method increases the recommendation precision for very Top-N results.Applicable for single ...
Local Item-Item Models For Top-N Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsItem-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in ...
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