Time-aware topic recommendation based on micro-blogs

Liang, Huizhi; Xu, Yan; Tjondronegoro, Dian; Christen, Peter

doi:10.1145/2396761.2398492

Cited by 45 publications

(23 citation statements)

References 15 publications

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“…The documents in qi.R1 are sorted in descending order of their arrival times. In particular, for each document di we store: (1) Document id; (2) Text relevance between qi and di; (3) Accumulated similarities of di, computed by Equation 24, is the sum of similarities between di and the documents in qi.R with arrival times earlier than di.…”

Section: Query Results Indexmentioning

confidence: 99%

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Diversity-Aware Top-k Publish/Subscribe for Text Stream

Chen

Cong

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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Massive amount of text data are being generated by a huge number of web users at an unprecedented scale. These data cover a wide range of topics. Users are interested in receiving a few up-todate representative documents (e.g., tweets) that can provide them with a wide coverage of different aspects of their query topics. To address the problem, we consider the Diversity-Aware Topk Subscription (DAS) query. Given a DAS query, we continuously maintain an up-to-date result set that contains k most recently returned documents over a text stream for the query. The DAS query takes into account text relevance, document recency, and result diversity. We propose a novel solution to efficiently processing a large number of DAS queries over a stream of documents. We demonstrate the efficiency of our approach on realworld dataset and the experimental results show that our solution is able to achieve a reduction of the processing time by 60-75% compared with two baselines. We also study the effectiveness of the DAS query.

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Section: Query Results Indexmentioning

confidence: 99%

“…It is introduced in [23] and is applied (e.g., [2,24,32]) as the measurement of recency for stream data. Based on the experimental studies [14], the exponential decay function has been shown to be effective in blending the recency and text relevancy of documents.…”

Section: Problem Statementmentioning

confidence: 99%

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Chen

Cong

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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“…Furthermore, our work focuses on a secondary item unit -version updates, which is a separate entity from primary item (i.e., the app). Liang et al [14] proposed a method to capture the temporal dynamics of relevant topics in micro-blogs (e.g., Twitter) where a topic centers around a certain theme such as the U.S. presidential election or Kate Middleton's baby which, in the micro-blogging community, may change quickly with time. Our work differs from theirs as the "items" in their system are the topics, which is an indefinite discourse.…”

Section: Time-sensitive Recommendationmentioning

confidence: 99%

New and improved

Lin

Sugiyama

Kan

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Existing recommender systems usually model items as static -unchanging in attributes, description, and features. However, in domains such as mobile apps, a version update may provide substantial changes to an app as updates, reflected by an increment in its version number, may attract a consumer's interest for a previously unappealing version. Version descriptions constitute an important recommendation evidence source as well as a basis for understanding the rationale for a recommendation. We present a novel framework that incorporates features distilled from version descriptions into app recommendation. We use a semi-supervised topic model to construct a representation of an app's version as a set of latent topics from version metadata and textual descriptions. We then discriminate the topics based on genre information and weight them on a per-user basis to generate a version-sensitive ranked list of apps for a target user. Incorporating our version features with stateof-the-art individual and hybrid recommendation techniques significantly improves recommendation quality. An important advantage of our method is that it targets particular versions of apps, allowing previously disfavored apps to be recommended when user-relevant features are added.

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“…Many techniques like tensor factorization [22,23,26] and graph model [5,7] have been proposed and applied to different social tagging systems like Flickr and Delicious. For Twitter, both user-based recommendation [4,14] and tweet-based recommendation have been proposed for hashtag recommendation [13,16,25,32]. Next, we briefly survey tweet-based recommendation for being more relevant to our work.…”

Section: Hashtags In Twittermentioning

confidence: 99%

Tagging Your Tweets

Sun

Yuan

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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The adoption of hashtags in major social networks including Twitter, Facebook, and Google+ is a strong evidence of its importance in facilitating information diffusion and social chatting. To understand the factors (e.g., user interest, posting time and tweet content) that may affect hashtag annotation in Twitter and to capture the implicit relations between latent topics in tweets and their corresponding hashtags, we propose two PLSA-style topic models to model the hashtag annotation behavior in Twitter. Content-Pivoted Model (CPM) assumes that tweet content guides the generation of hashtags while Hashtag-Pivoted Model (H PM) assumes that hashtags guide the generation of tweet content. Both models jointly incorporate user, time, hashtag and tweet content in a probabilistic framework. The PLSA-style models also enable us to verify the impact of social factor on hashtag annotation by introducing social network regularization in the two models. We evaluate the proposed models using perplexity and demonstrate their effectiveness in two applications: retrospective hashtag annotation and related hashtag discovery. Our results show that H PM outperforms CPM by perplexity and both user and time are important factors that affect model performance. In addition, incorporating social network regularization does not improve model performance. Our experimental results also demonstrate the effectiveness of our models in both applications compared with baseline methods.

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Time-aware topic recommendation based on micro-blogs

Cited by 45 publications

References 15 publications

Diversity-Aware Top-k Publish/Subscribe for Text Stream

Diversity-Aware Top-k Publish/Subscribe for Text Stream

New and improved

Tagging Your Tweets

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