Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user's previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for Top-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation.
|Original language||English (US)|
|Title of host publication||SIAM International Conference on Data Mining 2015, SDM 2015|
|Editors||Suresh Venkatasubramanian, Jieping Ye|
|Publisher||Society for Industrial and Applied Mathematics Publications|
|Number of pages||9|
|State||Published - 2015|
|Event||SIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada|
Duration: Apr 30 2015 → May 2 2015
|Name||SIAM International Conference on Data Mining 2015, SDM 2015|
|Other||SIAM International Conference on Data Mining 2015, SDM 2015|
|Period||4/30/15 → 5/2/15|
Bibliographical noteFunding Information:
This work was supported in part by NSF (IIS-0905220, OCI-1048018, CNS-1162405, IIS-1247632, IIP-1414153, IIS-1447788), Army Research Office (W911XF-14-1-0316), Samsung Research America, and the Digital Technology Center at the University of Minnesota.
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