Listwise Collaborative Filtering

Huang, Shanshan; Wang, Shuaiqiang; Liu, Tie-Yan; Ma, Jun; Chen, Zhumin; Veijalainen, Jari

doi:10.1145/2766462.2767693

Cited by 33 publications

(38 citation statements)

References 23 publications

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“…Pair-wise methods (e.g., BPR [5]), learn binary classifiers that compare ordered pairs of items to decide whether the first item is preferred to the second. The applicability of such methods is limited by the high computational cost of pair-wise comparisons of userrated items in generating the training samples for the binary classifiers [6]. List-wise methods leverage the entire list of items consumed by the users to optimize a list-wise ranking loss function or the probability of permutations that map items to ranks [7], [8].…”

Section: A Related Workmentioning

confidence: 99%

Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems

Liang

Dong

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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We propose Top-N-Rank, a novel family of listwise Learning-to-Rank models for reliably recommending the N top-ranked items. The proposed models optimize a variant of the widely used discounted cumulative gain (DCG) objective function which differs from DCG in two important aspects: (i) It limits the evaluation of DCG only on the top N items in the ranked lists, thereby eliminating the impact of low-ranked items on the learned ranking function; and (ii) it incorporates weights that allow the model to leverage multiple types of implicit feedback with differing levels of reliability or trustworthiness. Because the resulting objective function is non-smooth and hence challenging to optimize, we consider two smooth approximations of the objective function, using the traditional sigmoid function and the rectified linear unit (ReLU). We propose a family of learning-to-rank algorithms (Top-N-Rank) that work with any smooth objective function. Then, a more efficient variant, Top-N-Rank.ReLU, is introduced, which effectively exploits the properties of ReLU function to reduce the computational complexity of Top-N-Rank from quadratic to linear in the average number of items rated by users. The results of our experiments using two widely used benchmarks, namely, the MovieLens data set and the Amazon Video Games data set demonstrate that: (i) The "top-N truncation" of the objective function substantially improves the ranking quality of the top N recommendations; (ii) using the ReLU for smoothing the objective function yields significant improvement in both ranking quality as well as runtime as compared to using the sigmoid; and (iii) Top-N-Rank.ReLU substantially outperforms the well-performing list-wise ranking methods in terms of ranking quality.

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Section: A Related Workmentioning

confidence: 99%

Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems

Liang

Dong

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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“…They usually obtain the user similarity matrix based on the whole item lists instead of the item pairs. For instance, ListCF [13] adopts a permutation probability model and takes the divergence between two users' probability distributions as their similarity.…”

Section: Memory-based Methods For Top-n Recommendationmentioning

confidence: 99%

“…An example of typical pairwise approaches in collaborative filtering is Bayesian personalized ranking (BPR) [11] adopted in our coarse-to-fine transfer learning framework. As for listwise approaches [13], the distance or divergence between the predicted item list and the true item list is to be minimized. In addition to standard CF algorithms above, some other works integrate various CF approaches in order to achieve a much better performance, e.g., transfer to rank (ToR) [14].…”

Section: Introductionmentioning

confidence: 99%

Transfer to Rank for Top-N Recommendation

Dai

Zhang

Pan

et al. 2020

IEEE Trans. Big Data

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In this paper, we study top-N recommendation by exploiting users' explicit feedback such as 5-star numerical ratings, which has been overlooked to some extent in the past decade. As a response, we design a novel and generic transfer learning based recommendation framework coarse-to-fine transfer to rank (CoFiToR), which is a significant extension of a very recent work called transfer to rank (ToR). The key idea of our solution is modeling users' behaviors by simulating users' shopping processes. Therefore, we convert the studied ranking problem to three subtasks corresponding to three specific questions, including (i) whether an item will be examined by a user, (ii) how an item will be scored by a user, and (iii) whether an item will finally be purchased by a user. Based on this new conversion, we then develop a three-staged solution that progressively models users' preferences from a coarse granularity to a fine granularity. In each stage, we adopt an appropriate recommendation algorithm with pointwise or pairwise preference assumption to answer each question in order to seek an effective and efficient overall solution. Empirical studies on two large and public datasets showcase the merits of our solution in comparison with the state-of-the-art methods.

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“…Weimer et al [18] proposed an algorithm called CoFiRank which directly optimized the evaluation criteria NDCG. Huang et al [19] directly predicted a total order of items for each user based on similar users' probability distributions over permutations of the items.…”

Section: Ltr-based Cf Algorithmsmentioning

confidence: 99%

Recommendations Based on Listwise Learning-to-Rank by Incorporating Social Information

2018

KSII TIIS

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Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two

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Listwise Collaborative Filtering

Cited by 33 publications

References 23 publications

Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems

Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems

Transfer to Rank for Top-N Recommendation

Recommendations Based on Listwise Learning-to-Rank by Incorporating Social Information

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