Browsing by Subject "Recommender"

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    Database management system support for collaborative filtering recommender systems
    (2014-08) Sarwat, Mohamed
    Recommender systems help users identify useful, interesting items or content (data)from a considerably large search space. By far, the most popular recommendation technique used is collaborative filtering which exploits the users' opinions (e.g., movie ratings) and/or purchasing (e.g., watching, reading) history in order to extract a set of interesting items for each user. Database Management Systems (DBMSs) do not provide in-house support for recommendation applications despite their popularity. Existing recommender system architectures either do not employ a DBMS at all or only uses it as a data store whereas the recommendation logic is implemented in-full outside the database engine. Incorporating the recommendation functionality inside the DBMS kernel is beneficial for the following reasons: (1) Many recommendation algorithms take as input structured data (users, items, and user historical preferences) that could be adequately stored and accessed using a database system. (2) The In-DBMS approach facilitates applying the recommendation functionality and typical database operations(e.g., Selection, Join) side-by-side. That allows application developers to go beyond traditional recommendation applications, e.g., "Recommend to Alice ten movies", and flexibly define Arbitrary Recommendation scenarios like "Recommend ten nearby restaurants to Alice" and "Recommend to Bob ten movies watched by her friends". (3) Once the recommendation functionality lives inside the database kernel, the recommendation application takes advantage of the DMBS inherent features (e.g., query optimization, materialized views, indexing) provided by the storage manager and query execution engine.This thesis studies the incorporation of the recommendation functionality inside the core engine of a database management system. This is a major departure from existing recommender system architectures that are implemented on-top of a database engines using either SQL queries or stored procedures. The on-top approach does not harness the full power of the database engine (i.e., query execution engine, storage manager)since it always generates recommendations first and then performs other database operations. Ideas developed in this thesis are implemented inside RecDB ; an opensource recommendation engine built entirely inside PostgreSQL (open source relational database system).
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    Personalized Book Retrieval System Using Amazon-LibraryThing Collection
    (2014-08) Ravva, Venkataravikiran
    Information retrieval is the science of retrieving documents or information from a corpus based on the need of user. Selecting a book from a collection of available books based on its topical relevance to the query may not give us the "best" (or all the "best") such book(s). However, by including social data, such as popularity, reviws and ratings, may improve the results. So we include social data with book metadata for this purpose. The main goal of this research is to provide a book retrieval system for the Social Book Search (SBS) Track of the INEX forum. For the SBS track, participants are provided with an XML collection of data from Amazon and LibraryThing (LT) forum, a set of topics from the LT forum enriched with user catalogue data (i.e., books that the topic creator has in his LibraryThing personal catalogue), and anonymous user profiles. Participants must devise a system which provides the ISBN/work IDs of the books which are relevant to the topic creator. For this purpose, we designed a recommender system which provides personalized search results.