A classical predictive modeling approach for task "Who rated what?" of the KDD CUP 2007

Sueiras, Jorge; Salafranca, Alfonso; Flórez, José Luis

doi:10.1145/1345448.1345461

Cited by 5 publications

(9 citation statements)

References 3 publications

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“…One interesting hybrid method is to improve Top-N recommendations by combining recommendation results from several methods by formulating it as an optimization problem [11]. Logistic Regression is one popular model to optimize the predictions based on more than one predictor [12], [13], [14].…”

Section: A Related Workmentioning

confidence: 99%

Learning User Preference Patterns for Top-N Recommendations

Ren¹,

Li²,

Zhou³

2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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In this paper, we observe that the user preference styles tend to change regularly following certain patterns. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the stateof-the-art algorithms on the Top-N recommendation in terms of accuracy.

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Section: A Related Workmentioning

confidence: 99%

Learning User Preference Patterns for Top-N Recommendations

Ren¹,

Li²,

Zhou³

2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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“…Typical example of each type is shown in the right part of the figure. A success recommendation should contain type A B and D. We choose user/item-based Pearson Correlation Coefficient (PCC) [4,29], Aspect Model (AM) [16], PMF [28] and EigenRank [20] as quality-based algorithms; and within relevance-based algorithms, an association-based method [7] and a hitting-frequency-based method [33] are chosen. We randomly choose 40,000 users for training and 10,000 users (given their first 10 ratings) for testing.…”

Section: Quality Vs Relevancementioning

confidence: 99%

“…Within relevance-based algorithms, we choose an associationbased method [7] and a hitting-frequency-based method [33]. Association and hitting frequency have been demonstrated as two competitive features in relevance-based approaches.…”

Section: Rationale Of Basic Approaches Selectionmentioning

confidence: 99%

“…In other words, for two items, a user is likely to hit the one with more hitting count. This feature has been demonstrated effective in recent relevancebased recommendation competition KDD-cup 2007 [18,33].…”

Section: Rationale Of Basic Approaches Selectionmentioning

confidence: 99%

“…Typical criteria include relevance, coverage, diversity, etc. For example, the work of [7] and the tasks in the KDD-cup 2007 [18,27,33] emphasize on optimizing for recommending items with more relevance or user interests, which are relevance-based algorithms. Relevance is a different criterion from the criterion of quality.…”

Section: Introductionmentioning

confidence: 99%

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Cmap

Xin

Lyu

King

2011

Proceedings of the Fourth ACM International Conference on Web Search and Data Mining

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The research issue of recommender systems has been treated as a classical regression problem over the decades and has obtained a great success. In the next generation of recommender systems, multi-criteria recommendation has been predicted as an important direction. Different from traditional recommender systems that aim particularly at recommending high-quality items evaluated by users' ratings, in multi-criteria recommendation, quality only serves as one criterion, and many other criteria such as relevance, coverage, and diversity should be simultaneously optimized. Although recently there is work investigating each single criterion, there is rarely any literature that reports how each single criterion impacts each other and how to combine them in real applications. Thus in this paper, we study the relationship of two criteria, quality and relevance, as a preliminary work in multi-criteria recommendation. We first give qualitative and quantitative analysis of competitive quality-based and relevance-based algorithms in these two criteria to show that both algorithms cannot work well in the opposite criteria. Then we propose an integrated metric and finally investigate how to combine previous work together into an unified model. In the combination, we introduce a Continuous-time MArkov Process (CMAP) algorithm for ranking, which enables principled and natural integration with features derived from both quality-based and relevance-based algorithms. Through experimental verification, the combined methods can significantly outperform either single quality-based or relevance-based algorithms in the integrated metric and the CMAP model outperforms traditional combination methods by around 3%. Its linear complexity with respect to the number of users and items leads to satisfactory performance, as demonstrated by the around 7-hour computational time for over 480k users and almost 20k items.

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Improving Top-N Recommendations with User Consuming Profiles

Ren

Zhou

2012

Lecture Notes in Computer Science

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A classical predictive modeling approach for task "Who rated what?" of the KDD CUP 2007

Cited by 5 publications

References 3 publications

Learning User Preference Patterns for Top-N Recommendations

Learning User Preference Patterns for Top-N Recommendations

Cmap

Improving Top-N Recommendations with User Consuming Profiles

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