Recommendation in Internet Forums and Blogs
Wang, Jia and Li, Qing and Chen, Yuanzhu Peter and Lin, Zhangxi

Article Structure

Abstract

The variety of engaging interactions among users in social medial distinguishes it from traditional Web media.

Introduction

In the past twenty years, the Web has evolved from a framework of information dissemination to a social interaction facilitator for its users.

Related Work

In a broader context, a related problem is content-based information recommendation (or filtering).

System Design

In this section, we present a mechanism for recommendation in Internet forums and blogs.

Experimental Evaluation

To evaluate the effectiveness of our proposed recommendation mechanism, we carry out a series of experiments on two synthetic data sets, collected from Internet forums and blogs, respectively.

Conclusion and Future Work

The Web has become a platform for social networking, in addition to information dissemination at its earlier stage.

Topics

social media

Appears in 15 sentences as: social media (13) social medial (1) “social media” (1)
In Recommendation in Internet Forums and Blogs
  1. The variety of engaging interactions among users in social medial distinguishes it from traditional Web media.
    Page 1, “Abstract”
  2. Such a feature should be utilized while attempting to provide intelligent services to social media participants.
    Page 1, “Abstract”
  3. One of the most important observation from this work is that semantic contents of user comments can play a fairly different role in a different form of social media .
    Page 1, “Abstract”
  4. In a more general context, Web is one of the most important carriers for “social media” , e. g. In-
    Page 1, “Introduction”
  5. Various engaging interactions among users in social media differentiate it from traditional Web sites.
    Page 1, “Introduction”
  6. Such characteristics should be utilized in attempt to provide intelligent services to social media users.
    Page 1, “Introduction”
  7. In this work, we present a framework to recommend relevant information in Internet forums and blogs using user comments, one of the most representative recordings of user behaviors in these forms of social media .
    Page 2, “Introduction”
  8. Most recent researches on information recommendation in social media focus on the blogosphere.
    Page 2, “Related Work”
  9. different social media corpora (Section 4).
    Page 3, “Related Work”
  10. This simulates the scenario of recommending relevant news from traditional media to social media users for their further reading.
    Page 5, “Experimental Evaluation”
  11. In this part, we explore the contribution of user authority and comments in social media recommender.
    Page 7, “Experimental Evaluation”

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language models

Appears in 3 sentences as: language model (1) language models (2)
In Recommendation in Internet Forums and Blogs
  1. The second one, LM, is based on statistical language models for relevant information retrieval (Ponte and Croft, 1998).
    Page 6, “Experimental Evaluation”
  2. bilistic language model for each article, and ranks them on query likelihood, i.e.
    Page 6, “Experimental Evaluation”
  3. By combining such information with traditional statistical language models , it is capable of suggesting relevant articles that meet the dynamic nature of a discussion in social media.
    Page 8, “Conclusion and Future Work”

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LM

Appears in 3 sentences as: LM (3)
In Recommendation in Internet Forums and Blogs
  1. The second one, LM , is based on statistical language models for relevant information retrieval (Ponte and Croft, 1998).
    Page 6, “Experimental Evaluation”
  2. Okapi 0.827 0.833 0.807 0.751 Forum LM 0.804 0.833 0.807 0.731 Our 0.967 0.967 0.9 0.85
    Page 6, “Experimental Evaluation”
  3. Okapi 0.733 0.651 0.667 0.466 Blog LM 0.767 0.718 0.70 0.524 Our 0.933 0.894 0.867 0.756
    Page 6, “Experimental Evaluation”

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PageRank

Appears in 3 sentences as: PageRank (3)
In Recommendation in Internet Forums and Blogs
  1. To do that, we employ a variant of the PageRank algorithm (Erin and Page, 1998).
    Page 3, “System Design”
  2. Inline with the PageRank algorithm, we define the authority of user as
    Page 3, “System Design”
  3. Considering the semantic similarity between nodes, we use another variant of the PageRank algorithm to calculate the weight of comment
    Page 4, “System Design”

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semantic similarity

Appears in 3 sentences as: semantic similarity (3)
In Recommendation in Internet Forums and Blogs
  1. On the one hand, the semantic similarity between two nodes can be measured with any commonly adopted metric, such as cosine similarity and J accard coefficient (Baeza—Yates and Ribeiro-Neto, 1999).
    Page 3, “System Design”
  2. Considering the semantic similarity between nodes, we use another variant of the PageRank algorithm to calculate the weight of comment
    Page 4, “System Design”
  3. semantic similarity , reply, and quotation.
    Page 7, “Experimental Evaluation”

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