PS2: Translation-based factorization machines for sequential
Translation-based factorization machines for sequential recommendation
Rajiv Pasricha, Julian McAuley
10.1145/3240323.3240356
Sequential recommendation algorithms aim to predict users' future behavior given their historical interactions. A recent line of work has achieved state-of-the-art performance on sequential recommendation tasks by adapting ideas from metric learning and knowledge-graph completion. These algorithms replace inner products with low-dimensional embeddings and distance functions, employing a simple translation dynamic to model user behavior over time. In this paper, we propose TransFM, a model that combines translation and metric-based approaches for sequential recommendation with Factorization Machines (FMs). Doing so allows us to reap the benefits of FMs (in particular, the ability to straightforwardly incorporate content-based features), while enhancing the state-of-the-art performance of translation-based models in sequential settings. Specifically, we learn an embedding and translation space for each feature dimension, replacing the inner product with the squared Euclidean distance to measure the interaction strength between features. Like FMs, we show that the model equation for TransFM can be computed in linear time and optimized using classical techniques. As TransFM operates on arbitrary feature vectors, additional content information can be easily incorporated without significant changes to the model itself. Empirically, the performance of TransFM significantly increases when taking content features into account, outperforming state-of-the-art models on sequential recommendation tasks for a wide variety of datasets.
Видео PS2: Translation-based factorization machines for sequential канала ACM RecSys
Rajiv Pasricha, Julian McAuley
10.1145/3240323.3240356
Sequential recommendation algorithms aim to predict users' future behavior given their historical interactions. A recent line of work has achieved state-of-the-art performance on sequential recommendation tasks by adapting ideas from metric learning and knowledge-graph completion. These algorithms replace inner products with low-dimensional embeddings and distance functions, employing a simple translation dynamic to model user behavior over time. In this paper, we propose TransFM, a model that combines translation and metric-based approaches for sequential recommendation with Factorization Machines (FMs). Doing so allows us to reap the benefits of FMs (in particular, the ability to straightforwardly incorporate content-based features), while enhancing the state-of-the-art performance of translation-based models in sequential settings. Specifically, we learn an embedding and translation space for each feature dimension, replacing the inner product with the squared Euclidean distance to measure the interaction strength between features. Like FMs, we show that the model equation for TransFM can be computed in linear time and optimized using classical techniques. As TransFM operates on arbitrary feature vectors, additional content information can be easily incorporated without significant changes to the model itself. Empirically, the performance of TransFM significantly increases when taking content features into account, outperforming state-of-the-art models on sequential recommendation tasks for a wide variety of datasets.
Видео PS2: Translation-based factorization machines for sequential канала ACM RecSys
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Building public service recommenders: Logbook of a journeyTops, Bottoms, and Shoes: Building Capsule Wardrobes via Cross-Attention Tensor NetworkPS 5: Latent Factor Models and Aggregation Operators for Collaborative Filtering in Reciprocal RecomWorkshop on Context-Aware Recommender SystemsPaper Session 4: Domain Adaptation in Display Advertising: An Application for Partner Cold-StartMitigating Confounding Bias in Recommendation via Information BottleneckPS 7: Eliciting pairwise preferences in recommender systems Saikishore KallooriPS 6: Judging similarity: a user-centric study of related item recommendations Yuan YaoRecSys 2015 Session 4b: AlgorithmsBoosting Local Recommendations With Partially Trained Global ModelPessimistic Reward Models for Off-Policy Learning in RecommendationLearning a voice-based conversational recommender using offline policy optimizationSession 9: Timely Personalization at Peloton:System and Algorithm for Boosting Time Relevant ContentRecSys 2020 Session P2B: Evaluating and Explaining RecommendationsRecSys 2020 Session P5B: Real World Applications IIPrivacy Preserving Collaborative Filtering by Distributed MediationRecSys 2016: Paper Session 7 - Behaviorism is Not Enough: Better RecommendationsRecSys 2016: Paper Session 5 - Mechanism Design for Personalized Recommender SystemsRecSys 2020 Session P5A: Real World Applications IIDebiased Off-Policy Evaluation for Recommender Systems