RecStore: an extensible and adaptive framework for online recommender queries inside the database engine

  title={RecStore: an extensible and adaptive framework for online recommender queries inside the database engine},
  author={Justin J. Levandoski and Mohamed Sarwat and Mohamed F. Mokbel and Michael D. Ekstrand},
  booktitle={International Conference on Extending Database Technology},
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… 

Figures and Tables from this paper

RECATHON: A Middleware for Context-Aware Recommendation in Database Systems

Experimental results based on an actual prototype of RECATHon, built inside Postgre SQL, using real Movie Lens and Foursquare data show that RECATHON exhibits real time performance for large-scale multidimensional recommendation.

RecDB: towards DBMS support for online recommender systems

This paper proposes RecDB; a fully fledged database system that provides online recommendation to users using existing open source database system Apache Derby, and uses Sindbad; a Location-Based Social Networking system developed at University of Minnesota to showcase the effectiveness of RecDB.

Database System Support for Personalized Recommendation Applications

The anatomy of RecDB an open source PostgreSQLbased system that provides a unified approach for declarative data recommendation inside the database engine and shows that a recommendation-aware database engine, i.e., RecDB, outperforms the classic approach that implements the recommendation logic on-top of thedatabase engine in various recommendation applications.

Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale

VMIS-kNN is proposed, an adaptation of a state-of-the-art nearest neighbor approach to session-based recommendation, which leverages a prebuilt index to compute next-item recommendations with low latency in scenarios with hundreds of millions of clicks to search through.

Efficient Incremental Cooccurrence Analysis for Item-Based Collaborative Filtering

This work proposes an efficient incremental algorithm for item-based collaborative filtering based on cooccurrence analysis that is an order of magnitude faster than existing open source recommender libraries on many datasets, and at the same time scales to high dimensional datasets which these existing recommenders fail to process.

Investigating Recommender Systems in OSNs

A model of recommender-based systems that consume available public socialize networks information, implements it with database for customize and personal recommendations and method of cold start problem are discussed.

Learnings from a Retail Recommendation System on Billions of Interactions at

An A/B test on the live platform with more than 19 million user sessions confirms that the latency reduction of the recommender system correlates with a significant increase in business-relevant metrics, and discusses the implications of the findings with respect to real world recommendation systems and future research on scalable session-based recommendation.

Toward a scale-out data-management middleware for low-latency enterprise computing

Three technologies related to the issues and optimizations of key-value data object store and access are described, which form some necessary building blocks for a next-generation data-centric middleware for integrated transaction and analytic workloads.

Generating Top-k Packages via Preference Elicitation

This work develops an efficient algorithm for generating top-k packages using the learned utility function, where the rank ordering respects any of a variety of ranking semantics proposed in the literature.

Microblogs data management: a survey

This paper reviews core components that enable large-scale querying and indexing for microblogs data, and discusses system-level issues and on-going effort on supporting microblogs through the rising wave of big data systems.



AWESOME - A Data Warehouse-based System for Adaptive Website Recommendations Recommendations: Item-to-Item Collaborative Filtering

This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.

An MDP-Based Recommender System

The use of an n-gram predictive model is suggested for generating the initial MDP, which induces a Markovchain model of user behavior whose predictive accuracy is greater than that of existing predictive models.

Hybrid Recommender Systems: Survey and Experiments

  • R. Burke
  • Computer Science
    User Modeling and User-Adapted Interaction
  • 2004
This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.

PocketLens: Toward a personal recommender system

The new PocketLens collaborative filtering algorithm along with five peer-to-peer architectures for finding neighbors are presented and evaluated in a series of offline experiments, showing that Pocketlens can run on connected servers, on usually connected workstations, or on occasionally connected portable devices, and produce recommendations that are as good as the best published algorithms to date.

Evaluation of Item-Based Top-N Recommendation Algorithms

The experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better.

Google news personalization: scalable online collaborative filtering

This paper describes the approach to collaborative filtering for generating personalized recommendations for users of Google News using MinHash clustering, Probabilistic Latent Semantic Indexing, and covisitation counts, and combines recommendations from different algorithms using a linear model.

Recommender Systems

Recommender systems can be defined as programs which attempt to recommend the most suitable items (products or services) to particular users (individuals or businesses) by predicting a user’s interest in an item based on related information about the items, the users and the interactions between items and users.

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main