Papers Multi-Domain Recommender Systems
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Everyone's a Winner! On Hyperparameter Tuning of Recommendation Models
The performance of a recommender system algorithm in terms of common offline accuracy measures often strongly depends on the chosen hyperparameters. Therefore, when comparing algorithms in offline experiments, we can ob…
Multi-Domain Recommender SystemsRecommendation SystemsExploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation
Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying …
Multi-Domain Recommender SystemsRecommendation SystemsRepresentation LearningTransfer LearningOne for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing techniques focus on single-target or dual-ta…
AllMulti-Domain Recommender SystemsRecommendation SystemsRepresentation LearningRecommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance
In this industry talk at ECIR'2022, we illustrate how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. Specifically, we present our system archite…
Collaborative FilteringMulti-Domain Recommender SystemsRecommendation SystemsDecentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations
Recommender Systems (RSs) are operated locally by different organizations in many realistic scenarios. If various organizations can fully share their data and perform computation in a centralized manner, they may signifi…
Multi-Domain Recommender SystemsPrivacy PreservingRecommendation SystemsDive into Deep Learning
This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating expositio…
Deep LearningMathMulti-Domain Recommender SystemsMicrosoft Recommenders: Tools to Accelerate Developing Recommender Systems
The purpose of this work is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. The open source repository provides…
Multi-Domain Recommender SystemsA Novel Hybrid Sequential Model for Review-based Rating Prediction
Nowadays, the online interactions between users and items become diverse, and may include textual reviews as well as numerical ratings. Reviews often express various opinions and sentiments, which can alleviate the spars…
Multi-Domain Recommender SystemsRecommendation SystemsDomain-to-Domain Translation Model for Recommender System
Recently multi-domain recommender systems have received much attention from researchers because they can solve cold-start problem as well as support for cross-selling. However, when applying into multi-domain items, alth…
modelMulti-Domain Recommender SystemsRecommendation SystemsTranslationOuter Product-based Neural Collaborative Filtering
In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimen…
Collaborative FilteringMulti-Domain Recommender SystemsRecommendation Systems