IMPROVING THE PERFORMANCE OF MATRIX FACTORIZATION-BASED RECOMMENDATION SYSTEM BY PROPOSING A VALUE AS INITIAL ITEM LATENT FEATURE

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Mohammadian Mojtaba; Forghani Yahya; Niazi Torshiz Masood

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2019 | Issue:5

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Title

IMPROVING THE PERFORMANCE OF MATRIX FACTORIZATION-BASED RECOMMENDATION SYSTEM BY PROPOSING A VALUE AS INITIAL ITEM LATENT FEATURE

Author(s)

Mohammadian Mojtaba | Forghani Yahya | Niazi Torshiz Masood

Keywords

RECOMMENDATION SYSTEM

MATRIX FACTORIZATION

INITIAL ITEM LATENT FEATURES

Abstract

RECOMMENDATION SYSTEMS ARE SUCCESSFUL PERSONALIZING TOOLS IN WEB. THEY ARE USED TO PROVIDE CONTENT AND SERVICES TAILORED TO INDIVIDUALS BASED ON KNOWLEDGE ABOUT THEIR PREFERENCES AND BEHAVIORS IN A SYSTEM WITH MANY OPTIONS. RECOMMENDATION SYSTEM IS A NECESSARY TOOL FOR BIG INTERNET SHOPPING SUCH AS DIGIKALA. ONE OF THE MOST IMPORTANT RECOMMENDATION METHOD IS MATRIX FACTORIZATION METHOD. IN MATRIX FACTORIZATION METHOD, THE LATENT FEATURES OF USERS AND ITEMS ARE DETERMINED IN SUCH A WAY THAT THE INNER PRODUCT OF THE LATENT FEATURES OF A USER WITH THE LATENT FEATURE OF AN ITEM IS EQUAL TO THAT USER'S RATING ON THAT ITEM. THE PERFORMANCE OF THIS METHOD DEPENDS ON THE INITIAL VALUES OF THE LATENT FEATURES OF ITEMS WHICH IS USUALLY SET RANDOMLY. IN THIS PAPER, WE PROPOSE A METHOD TO DETERMINE THE INITIAL VALUES OF ITEM LATENT FEATURES. OUR PROPOSED INITIAL VALUE IS AN APPROXIMATE SOLUTION OF OUR PROPOSED MATHEMATICAL MODEL WHICH IS THE SIMPLE VERSION OF MATRIX FACTORIZATION MODEL. EXPERIMENTAL RESULTS ON THE DATASETS MOVIELENS100K, ML-LATEST-SMALL, DOUBAN, EACHMOVIE SHOWS THAT THE PERFORMANCE OF OUR PROPOSED METHOD IS BETTER THAN THE TRADITIONAL METHODS.

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APA: Copy

Mohammadian, Mojtaba, Forghani, Yahya, & Niazi Torshiz, Masood. (2019). IMPROVING THE PERFORMANCE OF MATRIX FACTORIZATION-BASED RECOMMENDATION SYSTEM BY PROPOSING A VALUE AS INITIAL ITEM LATENT FEATURE. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949115/en

Vancouver: Copy

Mohammadian Mojtaba, Forghani Yahya, Niazi Torshiz Masood. IMPROVING THE PERFORMANCE OF MATRIX FACTORIZATION-BASED RECOMMENDATION SYSTEM BY PROPOSING A VALUE AS INITIAL ITEM LATENT FEATURE. 2019. Available from: https://sid.ir/paper/949115/en

IEEE: Copy

Mojtaba Mohammadian, Yahya Forghani, and Masood Niazi Torshiz, “IMPROVING THE PERFORMANCE OF MATRIX FACTORIZATION-BASED RECOMMENDATION SYSTEM BY PROPOSING A VALUE AS INITIAL ITEM LATENT FEATURE,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2019, [Online]. Available: https://sid.ir/paper/949115/en

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