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Details

  • Name

    AmirHossein Nabizadeh
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    06th January 2014
Publications

2017

RUTICO: Recommending Successful Learning Paths Under Time Constraints

Authors
Nabizadeh, AH; Jorge, AM; Leal, JP;

Publication
Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, UMAP 2017, Bratislava, Slovakia, July 09 - 12, 2017

Abstract

2016

Predicting User Preference Based on Matrix Factorization by Exploiting Music Attributes

Authors
Nabizadeh, AH; Jorge, AM; Tang, S; Yu, Y;

Publication
Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering, C3S2E '16, Porto, Portugal, July 20-22, 2016

Abstract
With the emergence of online Music Streaming Services (MSS) such as Pandora and Spotify, listening to music online became very popular. Despite the availability of these services, users face the problem of finding among millions of music tracks the ones that match their music taste. MSS platforms generate interaction data such as users' defined playlists enriched with relevant metadata. These metadata can be used to predict users' preferences and facilitate personalized music recommendation. In this work, we aim to infer music tastes of users by using personal playlist information. Characterizing users' taste is important to generate trustable recommendations when the amount of usage data is limited. Here, we propose to predict the users' preferred music feature's value (e.g. Genre as a feature has different values like P op, Rock, etc.) by modeling, not only usage information, but also music description features. Music attribute information and usage data are typically dealt with separately. Our method FPMF (Feature Prediction based on Matrix Factorization) treats music feature values as virtual users and retrieves the preferred feature values for real target users. Experimental results indicate that our proposal is able to handle the item cold start problem and can retrieve preferred music feature values with limited usage data. Furthermore, our proposal can be useful in recommendation explanation scenarios. © 2016 ACM.

2015

Long term goal oriented recommender systems

Authors
Nabizadeh, AH; Jorge, AM; Leal, JP;

Publication
WEBIST 2015 - 11th International Conference on Web Information Systems and Technologies, Proceedings

Abstract
The main goal of recommender systems is to assist users in finding items of their interest in very large collections. The use of good automatic recommendation promotes customer loyalty and user satisfaction because it helps users to attain their goals. Current methods focus on the immediate value of recommendations and are evaluated as such. This is insufficient for long term goals, either defined by users or by platform managers. This is of interest in recommending learning resources to learn a target concept, and also when a company is organizing a campaign to lead users to buy certain products or moving to a different customer segment. Therefore, we believe that it would be useful to develop recommendation algorithms that promote the goals of users and platform managers (e.g. e-shop manager, e-learning tutor, ministry of culture promotor). Accordingly, we must define appropriate evaluation methodologies and demonstrate the concept on practical cases.