Classical Music for Rock Fans?: Novel Recommendations for Expanding User Interests Makoto Nakatsuji, Yasuhiro Fujiwara, Akimichi Tanaka, Toshio Uchiyama,

Classical Music for Rock Fans?:Novel Recommendations for Expanding User In-terests

Makoto Nakatsuji, Yasuhiro Fujiwara, Akimichi Tanaka, Toshio Uchiyama, Ko Fu-jimura 1, Toru Ishida2

1NTT Cyber Solutions Laboratories, NTT Corporation 2Department of Social Informatics, Kyoto University

CIKM 2010

2011. 2. 11.

Summarized and Presented by Kim Chung Rim, IDS Lab., Seoul National University

Copyright 2010 by CEBT

Contents

Introduction

Goal

Concept Explanation

Novelty

User Interest Model

User Similarity

Evaluation

Conclusion & Discussion

2


Introduction

Recommender systems are widely used by content providers

Increases chance of commercial success

Many content providers adopt methods based on collab-orative filtering

3


Weakness of basic CF method

It is apt to recommend the types of items that have been accessed by the user

Rock music is more likely to be recommended when the user previously rated on rock music only.

However, users may have various interests other than items that he has rated before

User often needs recommendations of other types of items

4


Goal

The goal of this paper lies in three folds

Introducing a new measure ‘novelty’

Integrate the taxonomy-based user similarity to the basic CF algorithm

Identify items with higher novelty for the active user

5


Concept - Novelty

Novel items are

items that cannot be easily discovered by the user

For example, a user who is interested in Rock music cannot easily discover interesting items in Classical music

Novelty is calculated using Taxonomy of items

6


Concept – User Interest Model

Users who are interested in some items are also inter-ested in classes that include those items

Therefore it can be said that the rating values of items in a class reflect user’s interest of that class

Authors calculate user interest of a class C by simply ag-gregating the interest score of all subclasses of C

7


Concept – User Similarity

User similarity is measured using user interest model and the original CF method (user rating behavior)

Where

8


Concept – Similarity against Items

Similarity of users calculated using Pearson correlation

Can be any other similarity measures, such as

Cosine Similarity

Jaccard Similarity

9


Concept – Similarity against Classes

Similarity against Classes can be measured as following:

Where

10


Methodology - Relatedness

Using Similarity measure ,

A user graph can be generated where nodes are users and the edge weights being

Edge weights are normalized to represent probability to move to adjacent node

RWR is performed on the graph until convergence

Each node holds a probability that a walk from active user a will pass through user u on the graph (relatedness)

11


Methodology – Rating Prediction

Using relatedness scores obtained from the user graph,

topN nodes with highest relatedness score are selected

– Top 40 for Movie dataset, Top 30 for Music dataset

Ratings of items are recalculated

The relatedness score is used instead of

12


Evaluation - Datasets

Several Datasets are used for the experiment

Rating against movies

– MovieLens Dataset : 212,586 ratings from 943 users on 1,682 movies

Rating against non-Japanese music artists

– Music Dataset from Doblog : 48,695 ratings from 3,545 users on 21,214 artists

– Taxonomy provided from ListenJapan : There are 279 genres in the taxonomy

Rating against Japanese music artists

– Music Dataset from Doblog : 58,104 ratings from 2,800 users on 7,421 artists

– Taxonomy provided from ListenJapan : There are 153 genres in the taxonomy

13


Evaluation – Methodology

The Dataset D is randomly divided into two parts:

Training dataset T

Prediction dataset P

Users who have items whose classes are in P but not in T can be generated

Varying the ratio of T to D (T/D), previously explained al-gorithms are run to predict user rating

14


Evaluation - Measurement

To measure how accurate the rating prediction is,

MAE(Mean Absolute Error) is calculated

To measure the coverage of algorithms

15


Evaluation – Compared similarity measure

Pearson Correlation coefficient

Cosine-based approach

Method proposed by Ziegler(WWW 05)

Taxonomy (Jaccard&Pearson)

Taxonomy (Jaccard)

16


Results - Accuracy

17


Results - Accuracy

18


Results - Coverage

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Results - Coverage

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Conclusion & Discussion

This paper uses rating of item as well as the taxonomy of items to calculate the similarity between two users.

Using such similarity measure and RWR, users who are not similar to the active user but who the walk passes through frequently can be extracted.

Such users’ items are then used to identify items with high novelty to expand users’ interests

21


Thank you

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Documents

Classical Music for Rock Fans?: Novel Recommendations for Expanding User Interests Makoto Nakatsuji, Yasuhiro Fujiwara, Akimichi Tanaka, Toshio Uchiyama,