Session: Algorithms 2
Chair: Gediminas Adomavicius
Date: Saturday, October 24, 10:40-12:20
- Stacking recommendation engines with additional meta-features
by Xinlong Bao, Lawrence Bergman, Rich Thompson
In this paper, we apply stacking, an ensemble learning method, to the problem of building hybrid recommendation systems. We also introduce the novel idea of using runtime metrics which represent properties of the input users/items as additional meta-features, allowing us to combine component recommendation engines at runtime based on user/item characteristics. In our system, component engines are level-1 predictors, and a level-2 predictor is learned to generate the final prediction of the hybrid system. The input features of the level-2 predictor are predictions from component engines and the runtime metrics. Experimental results show that our system outperforms each single component engine as well as a static hybrid system. Our method has the additional advantage of removing restrictions on component engines that can be employed; any engine applicable to the target recommendation task can be easily plugged into the system.
- A unified approach to building hybrid recommender systems
by Asela Gunawardana, Christopher Meek
Content-based recommendation systems can provide recommendations for “cold-start” items for which little or no training data is available, but typically have lower accuracy than collaborative filtering systems. Conversely, collaborative filtering techniques often provide accurate recommendations, but fail on cold start items. Hybrid schemes attempt to combine these different kinds of information to yield better recommendations across the board.
We describe unified Boltzmann machines, which are probabilistic models that combine collaborative and content information in a coherent manner. They encode collaborative and content information as features, and then learn weights that reflect how well each feature predicts user actions. In doing so, information of different types is automatically weighted, without the need for careful engineering of features or for post-hoc hybridization of distinct recommender systems.
We present empirical results in the movie and shopping domains showing that unified Boltzmann machines can be used to combine content and collaborative information to yield results that are competitive with collaborative techniques in recommending items that have been seen before, and also effective at recommending cold-start items.
- Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
by Yue Shi, Martha Larson, Alan Hanjalic
An approach to user-based collaborative filtering is proposed that refines prediction of item ratings that is based on global user similarity by incorporating information derived from a more detailed user comparison made on the basis of Rated Item Pools (RIPs). The preference spectrum defined by items that a user has rated, and ranging from best-liked to most disliked items, is divided into item sets, or RIPs, which supply the basis for a fine-grained calculation of similarity between users. The RIP-based approach makes it possible for the model to take advantage of user tastes that are matched at one end of the spectrum, e.g., two users agree on favorites, without requiring complete correspondence of item ratings between user profiles. The approach improves rating prediction, as compared to a baseline that uses the global user similarity alone. It does not unduly inflate computational complexity or rely on external resources, common shortcomings of competing rating prediction methods. Cases in which the nearest neighbors are relatively dissimilar, known to be challenging for user-based collaborative filtering, demonstrate particularly substantial improvement. Performance is shown to be stable across the choice of neighborhood size, number of pools and relative pool size.
- Assessment of conversation co-mentions as a resource for software module recommendation
by Daniel Xiaodan Zhou, Paul Resnick
Conversation double pivots recommend target items related to a source item, based on co-mentions of source and target items in online forums. We deployed several variants on the drupal.org site that supports the Drupal open source community, and assessed them through clickthrough rates. A similarity metric based on correlation of mentions rather than mere co-occurrence reduced the problem of over-recommending the most popular modules, but additional corrections for recency and uniqueness of mentions were not helpful. Detection of more module mentions in conversations dramatically improved the quality of recommendations, even though the detection algorithm then had more false positives. Recommendations based on conversation co-mention were more effective than those based on co-installation, because co-installation data only led to recommendations of complementary modules and not substitutes. Recommendations based on co-mention were more effective than those based on text similarity matching for navigating from the most popular modules, but less effective than text matching for less popular modules.
RecSys 2009 (New York)
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