NDLI: Bridging memory-based collaborative filtering and text retrieval

Please wait, while we are loading the content...

Bridging memory-based collaborative filtering and text retrieval

Content Provider	Springer Nature Link
Author	Bellogín, Alejandro Wang, Jun Castells, Pablo
Copyright Year	2012
Abstract	When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users’ preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived. In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user’s ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering.
Starting Page	697
Ending Page	724
Page Count	28
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	16
Issue Number	6
e-ISSN	15737659
Language	English
Publisher	Springer Netherlands
Publisher Date	2012-11-17
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Recommender systems Text retrieval models Information Storage and Retrieval Document Preparation and Text Processing Data Mining and Knowledge Discovery Data Structures, Cryptology and Information Theory Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Probabilistic relevance ranking for collaborative filtering

Using rating matrix compression techniques to speed up collaborative recommendations

A pattern mining approach for information filtering systems

Defending recommender systems by influence analysis

Nearest-biclusters collaborative filtering based on constant and coherent values

Detecting shilling attacks in private environments

Constructing click models for search users

Modeling score distributions in information retrieval

Information retrieval evaluation using test collections

Bridging memory-based collaborative filtering and text retrieval

Similar Documents

Probabilistic relevance ranking for collaborative filtering

Using rating matrix compression techniques to speed up collaborative recommendations

A pattern mining approach for information filtering systems

Defending recommender systems by influence analysis

Nearest-biclusters collaborative filtering based on constant and coherent values

Detecting shilling attacks in private environments

Constructing click models for search users

Modeling score distributions in information retrieval

Information retrieval evaluation using test collections

Bridging memory-based collaborative filtering and text retrieval