List-wise probabilistic matrix factorization for recommendation

Liu, Juntao; Wu, Caihua; Yi, Xiong; Liu, Wenyu

doi:10.1016/j.ins.2014.03.063

Cited by 52 publications

(29 citation statements)

References 15 publications

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“…Other methods optimize discounted cumulative gain (DCG) or normalized DCG (NDCG) can be found in [10], [11]. Examples of list-wise methods that optimize the probability of permutations that map items to ranks include: ListPMF, which represents each user as a probability distribution of the permutations over rated items based on the Plackett-Luce model [12]; ListRank [13], which aims to identify a ranking permutation that minimizes the cross-entropy between the distribution of the observed ranking of items based on user ratings and the predicted rankings with respect to the topranked item; or methods that optimize the log-posterior over the predicted preference order with the observed preference orders [12]; and methods that leverage deep neural nets (e.g., [4]) to learn the non-linear interaction between user-item pairs (See [14] for a survey of such methods).…”

Section: A Related Workmentioning

confidence: 99%

“…The main contributions of this paper can be summarized as follows: We compared the performance of Top-N-Rank and Top-N-Rank.ReLU with several state-of-the-art list-wise LTR methods [3], [10], [12], [13], [16] using the MovieLens (20M) data set [17] and the Amazon video games data set [18]. All experiments were performed on Apache Spark cluster [19] and the raw data are stored on Hadoop Distribute File System (HDFS).…”

Section: B Overview and Contributionsmentioning

confidence: 99%

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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%

Section: B Overview and Contributionsmentioning

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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“…These include recommending item ratings [34], tags [12], documents [14], friends [5,15], experts [21,13] and many others in social networks. Various recommendation algorithms are exploited in these systems, ranging from the canonical collaborative filtering [16], graph-based propagation [12,14], to the new list-wise probabilistic matrix factorization [22], etc. The enthusiasm in this area is expected to last for the years to come as many new business models in social networks are highly dependent on recommendation accuracy.…”

Section: Recommendation In Social Networkmentioning

confidence: 99%

Friend recommendation with content spread enhancement in social networks

Wang

et al. 2015

Information Sciences

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“…[10,31] used view information to enrich positive samples. [6,25] proposed listwise ranking methods instead of pairwise ones. Hwang et al [20] utilized both implicit and explicit feedback data to improve the quality of negative sampling.…”

Section: Learning To Rankmentioning

confidence: 99%

Spectrum-enhanced Pairwise Learning to Rank

Qin

2019

The World Wide Web Conference

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To enhance the performance of the recommender system, side information is extensively explored with various features (e.g., visual features and textual features). However, there are some demerits of side information: (1) the extra data is not always available in all recommendation tasks; (2) it is only for items, there is seldom highlevel feature describing users. To address these gaps, we introduce the spectral features extracted from two hypergraph structures of the purchase records. Spectral features describe the similarity of users/items in the graph space, which is critical for recommendation. We leverage spectral features to model the users' preference and items' properties by incorporating them into a Matrix Factorization (MF) model.In addition to modeling, we also use spectral features to optimize. Bayesian Personalized Ranking (BPR) is extensively leveraged to optimize models in implicit feedback data. However, in BPR, all missing values are regarded as negative samples equally while many of them are indeed unseen positive ones. We enrich the positive samples by calculating the similarity among users/items by the spectral features. The key ideas are: (1) similar users shall have similar preference on the same item; (2) a user shall have similar perception on similar items. Extensive experiments on two real-world datasets demonstrate the usefulness of the spectral features and the effectiveness of our spectrum-enhanced pairwise optimization. Our models outperform several state-of-the-art models significantly.

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List-wise probabilistic matrix factorization for recommendation

Cited by 52 publications

References 15 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

Friend recommendation with content spread enhancement in social networks

Spectrum-enhanced Pairwise Learning to Rank

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