Content-Based Filtering Recommendation Algorithm Using HMM

Li, Hui; Cai, Fei; Liao, Zhifang

doi:10.1109/iccis.2012.112

Cited by 28 publications

(20 citation statements)

References 4 publications

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“…Thus, when users cannot provide suitable queries that accurately reflect their desired video, successful retrieval of a video becomes difficult [2]. In order to solve this problem, many video recommendation methods that do not require any queries have been studied, and they are broadly classified into two main types of method [3]: collaborative filtering [4], [5] and content-based filtering [6], [7]. In methods based on collaborative filtering, users who have similar preferences are found on the basis of evaluation scores given by the users.…”

Section: Introductionmentioning

confidence: 99%

Accurate Estimation of Personalized Video Preference Using Multiple Users' Viewing Behavior

Ito

Ogawa

Haseyama

2018

IEICE Trans. Inf. & Syst.

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Yoshiki ITO †a) , Student Member, Takahiro OGAWA †b) , and Miki HASEYAMA †c) , Members SUMMARY A method for accurate estimation of personalized video preference using multiple users' viewing behavior is presented in this paper. The proposed method uses three kinds of features: a video, user's viewing behavior and evaluation scores for the video given by a target user. First, the proposed method applies Supervised Multiview Spectral Embedding (SMSE) to obtain lower-dimensional video features suitable for the following correlation analysis. Next, supervised Multi-View Canonical Correlation Analysis (sMVCCA) is applied to integrate the three kinds of features. Then we can get optimal projections to obtain new visual features, "canonical video features" reflecting the target user's individual preference for a video based on sMVCCA. Furthermore, in our method, we use not only the target user's viewing behavior but also other users' viewing behavior for obtaining the optimal canonical video features of the target user. This unique approach is the biggest contribution of this paper. Finally, by integrating these canonical video features, Support Vector Ordinal Regression with Implicit Constraints (SVORIM) is trained in our method. Consequently, the target user's preference for a video can be estimated by using the trained SVORIM. Experimental results show the effectiveness of our method. key words: multiview approach, spectral embedding, canonical correlation analysis, video preference, viewing behavior *

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Section: Introductionmentioning

confidence: 99%

Accurate Estimation of Personalized Video Preference Using Multiple Users' Viewing Behavior

Ito

Ogawa

Haseyama

2018

IEICE Trans. Inf. & Syst.

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“…Machine learning is used for content based filtering. [6] In this paper Rongbo Du, Reihaneh Safavi Naini and Willy Susilo have done work on Web filtering which is used for text classification. The proposed algorithm is used to block or allow the web page which contains forbidden contents.…”

Section: Research Work Conducted On Web Content Filteringmentioning

confidence: 99%

Web Content Filtration Using Different Web Mining Techniques in Educational System: An Overview

Modi¹,

Jagtap²

2017

IJARCSSE

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Abstract-Internet is widely used as ICT tool in today's education. The web content filtering is essential tool used to filter offensive, unwanted web content from web pages. Internet is widely used in educational organisations as an ICT tool. To protect our tech savvy student from accessing adult sites, offensive, unwanted data in absence of teachers and parents in school as well as home. A strong filter is required to prevent this type of unsolicited activity. In this paper we review the existing filter tools and their techniques.

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“…Distance or dissimilarity metrics are very popular, e.g., such as in the systems proposed by Li et al (2012) or Veloso et al (2013). In the first case (Li et al, 2012), the authors rely on the Hamming distance to aggregate the items into clusters, apply a hidden Markov model to determine their probabilities and, finally, recommend items (films) similar to the user item history.…”

Section: Content-based Filtersmentioning

confidence: 99%

“…In the first case (Li et al, 2012), the authors rely on the Hamming distance to aggregate the items into clusters, apply a hidden Markov model to determine their probabilities and, finally, recommend items (films) similar to the user item history. In the second case (Veloso et al, 2013), the system recommends advertisements by determining, among other metrics, the Euclidean distance between the user profile and the candidate advertisements.…”

Section: Content-based Filtersmentioning

confidence: 99%

A multi-agent brokerage platform for media content recommendation

Veloso

Malheiro

Burguillo

2015

International Journal of Applied Mathematics and Computer Science

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Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.

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Content-Based Filtering Recommendation Algorithm Using HMM

Cited by 28 publications

References 4 publications

Accurate Estimation of Personalized Video Preference Using Multiple Users' Viewing Behavior

Accurate Estimation of Personalized Video Preference Using Multiple Users' Viewing Behavior

Web Content Filtration Using Different Web Mining Techniques in Educational System: An Overview

A multi-agent brokerage platform for media content recommendation

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