List-wise learning to rank with matrix factorization for collaborative filtering

By Yue Shi, Martha A. Larson, Alan Hanjalic. 2010

266 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xu Chen
2. Yongfeng Zhang
3. Weinan Zhang
4. Harald Steck
5. Hao Wang
6. Jun Wang
7. Xiangnan He
8. Yong Yu

In Information Systems

1. Xu Chen
2. Yongfeng Zhang
3. Xiangnan He
4. Harald Steck
5. Yue Shi
6. Charu C. Aggarwal
7. Hao Wang
8. Weinan Zhang

Citations in the corpus

• A Neural Autoregressive Approach to Collaborative Filtering (2016, background): “Put differently, the ranking loss in Equation 17 is defined on the ratings, while other learning-to-rank based CF meth-ods, such as (Shi et al., 2010), are on items, which is the crucial difference.”
• Gaussian Ranking by Matrix Factorization (2015, background): “There has been a large body of work on training collaborative filtering approaches as to maximize various ranking metrics, see eg [5, 8, 17, 2, 18, 10, 20, 21, 14, 13, 12].”
• Explainable Recommendation: A Survey and New Perspectives (2018, background): “Singh and Anand [2018] studied the post hoc explanations of learning-to-rank algorithms in term of web search.”
• Recommender Systems (2016, unknown): “CLiMF: This approach [545, 546] optimizes the mean-reciprocal rank (MRR), which has the tendency to obtain at least a few interesting items at the top of the list.”
• Diversity, Serendipity, Novelty, and Coverage (2016, background): “In recent years, the focus of RS research has shifted to such objectives as correctly ranking a set of items (known as the learning-to-rank problem [Shi et al. 2010]) as well as ensuring that the set of recommended items is diverse [Vargas et al.”
• CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (2012, background): “2.2 Learning to Rank Learning to Rank (LTR) has been an active research topic in Machine Learning, Information Retrieval [18] and Rec­ommender Systems [3, 25, 31].”
• Relational Collaborative Topic Regression for Recommender Systems (2015, methodology): “CF based methods [7], [11], [13], [18], [27], [30], [33], [35], [37], [39], [42] use past activities or preferences, such as the ratings on items given by users, for prediction, without using any user or product profiles.”
• Neural Rating Regression with Abstractive Tips Generation for Recommendation (2017, methodology): “• NMF:Non-negativeMatrix Factorization [18].”
• Learning to personalize trending image search suggestion (2014, background): “6 Many other proposed MF techniques to fit different rank-oriented optimization goals [18, 27, 30, 34] can also be adopted.”
• Optimizing top-n collaborative filtering via dynamic negative item sampling (2013, methodology): “In addition to the pair-wise CF approaches, the list-wise approaches inspired by Learning to Rank techniques for text retrieval have also been introduced (e.g., [8]).”
• Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, methodology): “CliMF Maximize reciprocal rank method for item ranking [37] LRMF List-wise learning to rank method for item ranking [36] NMF Non-negative matrix factorization [20] PMF Probabilistic matrix factorization [28] SoMF Trust propagation matrix factorization [15] TrMF Trust social matrix factorization [50]”
• Collaborative Denoising Auto-Encoders for Top-N Recommender Systems (2016, methodology): “Some models use list-wise objective functions [28, 22], but they are not as widely adopted as point-wise and pair-wise objectives.”