GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Materialized views: techniques, implementations, and applications
Materialized views: techniques, implementations, and applications
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Personalized systems: models and methods from an IR and DB perspective
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Making recommendations better: an analytic model for human-recommender interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
AWESOME: a data warehouse-based system for adaptive website recommendations
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
An MDP-based recommender system
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
RecDB: towards DBMS support for online recommender systems
PhD '12 Proceedings of the on SIGMOD/PODS 2012 PhD Symposium
Toward a scale-out data-management middleware for low-latency enterprise computing
IBM Journal of Research and Development
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Most recommendation methods (e.g., collaborative filtering) consist of (1) a computationally intense offline phase that computes a recommender model based on users' opinions of items, and (2) an online phase consisting of SQL-based queries that use the model (generated offline) to derive user preferences and provide recommendations for interesting items. Current application usage trends require a completely online recommender process, meaning the recommender model must update in real time as new opinions enter the system. To tackle this problem, we propose RecStore, a DBMS storage engine module capable of efficient online model maintenance. Externally, models managed by RecStore behave as relational tables, thus existing SQL-based recommendation queries remain unchanged while gaining online model support. RecStore maintains internal statistics and data structures aimed at providing efficient incremental updates to the recommender model, while employing an adaptive strategy for internal maintenance and load shedding to realize a balance between efficiency in updates or query processing based on system workloads. RecStore is also extensible, supporting a declarative syntax for defining recommender models. The efficacy of RecStore is demonstrated by providing the implementation details of three state-of-the-art collaborative filtering models. We provide an extensive experimental evaluation of a prototype of RecStore, built inside the storage engine of PostgreSQL, using a real-life recommender system workload.