Exploring Movie Recommendation System Using Cultural Metadata

Ah, Shinhyun; Shi, Chung-Kon

doi:10.1109/cw.2008.13

Cited by 10 publications

(9 citation statements)

References 16 publications

(8 reference statements)

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“…This work is novel in its use of micro-blogging information for recommendation. Our approach is related to a growing body of research on the potential for user-generated content to provide product recommendations [1,3,23]. This related research focuses mainly on more conventional, longform user reviews, whereas the work presented here focuses on the more challenging micro-blogging messages.…”

Section: Discussionmentioning

confidence: 99%

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Mining the real-time web: A novel approach to product recommendation

Esparza

O’Mahony

Smyth

2012

Knowledge-Based Systems

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Real-time web (RTW) services such as Twitter allow users to express their opinions and interests, often expressed in the form of short text messages providing abbreviated and highly personalized commentary in real-time. Although this RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research, it can contain useful consumer reviews on products, services and brands. This paper describes how Twitter-like short-form messages can be leveraged as a source of indexing and retrieval information for product recommendation. In particular, we describe how users and products can be represented from the terms used in their associated reviews. An evaluation performed on four di↵erent product datasets from the Blippr service shows the potential of this type of recommendation knowledge, and the experiments show that our proposed approach outperforms a more traditional collaborative-filtering based approach.

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Section: Discussionmentioning

confidence: 99%

“…Similar ideas are described in [3], which look at using user-generated movie reviews from IMDb in combination with movie meta-data (e.g. keywords, genres, plot outlines and synopses) as input for a movie recommender system.…”

Section: Related Workmentioning

confidence: 99%

Mining the real-time web: A novel approach to product recommendation

Esparza

O’Mahony

Smyth

2012

Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…Our approach is related to a growing body of research on the potential for user-generated content to inform recommendation [1,3,29]. This related research focuses mainly on more conventional, long-form user reviews, whereas the work presented in this paper focuses on the more challenging micro-blogging messages.…”

Section: Discussionmentioning

confidence: 99%

“…In Section 2, we describe related work that has been carried out on sentiment analysis and opinion mining of user-generated content. A description of the Blippr service 3 , which we use as our test domain, is presented in Section 3. Our recommender approach, based on RTW data, is detailed in Section 4 and the results of an empirical evaluation of the approach are given in Section 5.…”

mentioning

confidence: 99%

Effective Product Recommendation using the Real-Time Web

Esparza¹,

O’Mahony²,

Smyth³

2010

Research and Development in Intelligent Systems XXVII

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The so-called real-time web (RTW) is a web of opinions, comments, and personal viewpoints, often expressed in the form of short, 140-character text messages providing abbreviated and highly personalized commentary in real-time. Today, Twitter is undoubtedly the king of the RTW. It boasts 190 million users and generates in the region of 65m tweets per day 1 . This RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research but it is useful to consider its applicability to recommendation scenarios. In this paper we consider harnessing the real-time opinions of users, expressed through the Twitter-like short textual reviews available on the Blippr service (www.blippr.com). In particular we describe how users and products can be represented from the terms used in their associated reviews and describe experiments to highlight the recommendation potential of this RTW data-source and approach.

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“…Our approach is related to a growing body of research on the potential for UGC to inform recommendation [1,2,7,12]. This related research focuses mainly on more conventional, long-form user reviews, whereas the work presented in this paper focuses on the more challenging micro-blogging messages.…”

Section: Discussionmentioning

confidence: 99%

On the real-time web as a source of recommendation knowledge

Esparza

O’Mahony

Smyth

2010

Proceedings of the Fourth ACM Conference on Recommender Systems

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The so-called real-time web (RTW) is a web of opinions, comments, and personal viewpoints, often expressed in the form of short, 140-character text messages providing abbreviated and personalized commentary in real-time. Twitter is undoubtedly the king of the RTW. It boasts 100+ million users and generates in the region of 50m tweets per day. This RTW data is far from the structured data (ratings, product features, etc.) familiar to recommender systems research, but it is useful to consider its applicability to recommendation scenarios. In this short paper we describe an experiment to look at harnessing the real-time opinions of movie fans, expressed through the Twitter-like short textual reviews available on the Blippr service (www.blippr.com). In particular we describe how users and movies can be represented from the terms used in their associated reviews and describe a number of experiments to highlight the recommendation potential of this RTW data-source and approach.

show abstract

Exploring Movie Recommendation System Using Cultural Metadata

Cited by 10 publications

References 16 publications

Mining the real-time web: A novel approach to product recommendation

Mining the real-time web: A novel approach to product recommendation

Effective Product Recommendation using the Real-Time Web

On the real-time web as a source of recommendation knowledge

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