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Care to Comment? Recommendations for Commenting on News Stories
, 2012
"... Many websites provide commenting facilities for users to express their opinions or sentiments with regards to content items, such as, videos, news stories, blog posts, etc. Previous studies have shown that user comments contain valuable information that can provide insight on Web documents and may b ..."
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Many websites provide commenting facilities for users to express their opinions or sentiments with regards to content items, such as, videos, news stories, blog posts, etc. Previous studies have shown that user comments contain valuable information that can provide insight on Web documents and may be utilized for various tasks. This work presents a model that predicts, for a given user, suitable news stories for commenting. The model achieves encouraging results regarding the ability to connect users with stories they are likely to comment on. This provides grounds for personalized recommendations of stories to users who may want to take part in their discussion. We combine a content-based approach with a collaborative-filtering approach (utilizing users ’ co-commenting patterns) in a latent factor modeling framework. We experiment with several variations of the model’s loss function in order to adjust it to the problem domain. We evaluate the results on two datasets and show that employing co-commenting patterns improves upon using content features alone, even with as few as two available comments per story. Finally, we try to incorporate available social network data into the model. Interestingly, the social data does not lead to substantial performance gains, suggesting that the value of social data for this task is quite negligible.
Multi-Objective Ranking of Comments on Web
, 2012
"... With the explosion of information on any topic, the need for ranking is becoming very critical. Ranking typically depends on several aspects. Products, for example, have several aspects like price, recency, rating, etc. Product ranking has to bring the “best ” product which is recent and highly rate ..."
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With the explosion of information on any topic, the need for ranking is becoming very critical. Ranking typically depends on several aspects. Products, for example, have several aspects like price, recency, rating, etc. Product ranking has to bring the “best ” product which is recent and highly rated. Hence ranking has to satisfy multiple objectives. In this paper, we explore multi-objective ranking of comments using Hodge decomposition. While Hodge decomposition produces a globally consistent ranking, a globally inconsistent component is also present. We propose an active learning strategy for the reduction of this component. Finally, we develop techniques for online Hodge decomposition. We experimentally validate the ideas presented in this paper.
HeteroMF: Recommendation in Heterogeneous Information Networks using Context Dependent Factor Models ABSTRACT
"... With the growing amount of information available online, recommender systems are starting to provide a viable alternative and complement to search engines, in helping users to find objects of interest. Methods based on Matrix Factorization (MF) models are the state-of-the-art in recommender systems. ..."
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With the growing amount of information available online, recommender systems are starting to provide a viable alternative and complement to search engines, in helping users to find objects of interest. Methods based on Matrix Factorization (MF) models are the state-of-the-art in recommender systems. The input to MF is user feedback, in the form of a rating matrix. However, users can be engaged in interactions with multiple types of entities across different contexts, leading to multiple rating matrices. In other words, users can have interactions in a heterogeneous information network. Generally, in a heterogeneous network, entities from any two entity types can have interactions with a weight (rating) indicating the level of endorsement. Collective Matrix Factorization (CMF) has been proposed to address the recommendation problem in heterogeneous networks. However, a main issue with CMF is that entities share the same latent factor across different contexts.
Personalized sentiment classification based on latent individuality of microblog users
- In Proceedings of the 24th International Joint Conference on Artificial Intelligence
, 2015
"... Sentiment expression in microblog posts often re-flects user’s specific individuality due to different language habit, personal character, opinion bias and so on. Existing sentiment classification algo-rithms largely ignore such latent personal distinc-tions among different microblog users. Meanwhil ..."
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Sentiment expression in microblog posts often re-flects user’s specific individuality due to different language habit, personal character, opinion bias and so on. Existing sentiment classification algo-rithms largely ignore such latent personal distinc-tions among different microblog users. Meanwhile, sentiment data of microblogs are sparse for indi-vidual users, making it infeasible to learn effective personalized classifier. In this paper, we propose a novel, extensible personalized sentiment classi-fication method based on a variant of latent fac-tor model to capture personal sentiment variations by mapping users and posts into a low-dimensional factor space. We alleviate the sparsity of personal texts by decomposing the posts into words which are further represented by the weighted sentiment and topic units based on a set of syntactic units of words obtained from dependency parsing results. To strengthen the representation of users, we lever-age users following relation to consolidate the in-dividuality of a user fused from other users with similar interests. Results on real-world microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins. 1
SmartNews: Bringing order into comments chaos
"... Abstract: Various news sites exist today where internet audience can read the most recent news and see what other people think about. Most sites do not organize comments well and do not filter irrelevant content. Due to this limitation, readers who are interested to know other people’s opinion regar ..."
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Abstract: Various news sites exist today where internet audience can read the most recent news and see what other people think about. Most sites do not organize comments well and do not filter irrelevant content. Due to this limitation, readers who are interested to know other people’s opinion regarding any specific topic, have to manually follow relevant comments, reading and filtering a lot of irrelevant text. In this work, we introduce a new approach for retrieving and ranking the relevant comments for a given paragraph of news article and vice versa. We use Topic-Sensitive PageRank for ranking comments/paragraphs relevant for a user-specified paragraph/comment. The browser extension implementing our approach (called SmartNews) for Yahoo! News is publicly available. 1
Are Features Equally Representative? A Feature-Centric Recommendation
"... Typically a user prefers an item (e.g., a movie) because she likes certain features of the item (e.g., director, genre, pro-ducer). This observation motivates us to consider a feature-centric recommendation approach to item recommendation: instead of directly predicting the rating on items, we predi ..."
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Typically a user prefers an item (e.g., a movie) because she likes certain features of the item (e.g., director, genre, pro-ducer). This observation motivates us to consider a feature-centric recommendation approach to item recommendation: instead of directly predicting the rating on items, we predict the rating on the features of items, and use such ratings to derive the rating on an item. This approach offers several ad-vantages over the traditional item-centric approach: it incor-porates more information about why a user chooses an item, it generalizes better due to the denser feature rating data, it explains the prediction of item ratings through the predicted feature ratings. Another contribution is turning a principled item-centric solution into a feature-centric solution, instead of inventing a new algorithm that is feature-centric. This ap-proach maximally leverages previous research. We demon-strate this approach by turning the traditional item-centric la-tent factor model into a feature-centric solution and demon-strate its superiority over item-centric approaches. 1
Latent Factor Regressions for the Social Sciences∗
, 2014
"... In this paper I present a general framework for regression in the presence of complex dependence structures between units such as in time-series cross-sectional data, relational/network data, and spatial data. These types of data are challenging for standard multilevel models because they involve mu ..."
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In this paper I present a general framework for regression in the presence of complex dependence structures between units such as in time-series cross-sectional data, relational/network data, and spatial data. These types of data are challenging for standard multilevel models because they involve multiple types of structure (e.g. temporal effects and cross-sectional effects) which are interactive. I show that interactive latent factor models provide a powerful modeling alternative that can address a wide range of data types. Although related models have previously been proposed in several different fields, inference is typically cumbersome and slow. I introduce a class of fast variational inference algorithms that allow for models to be fit quickly and accurately. ∗For comments and discussions on various portions of this material I thank Adam Glynn, Justin Grim-mer, Gary King, Horacio Larreguy, Chris Lucas, John Marshall, Helen Milner, Brendan O’Connor, and Beth Simmons. Molly Roberts provided both enlightening discussions and code from her paper on robust standard errors. Special thanks to Dustin Tingley without whom this paper would not have been possible. An appendix containing additional details is available on my website: scholar.harvard.edu/bstewart
Connecting Comments and Tags: Improved Modeling of Social Tagging Systems
"... Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation can simplify and streamline the user experience, and by modeling user preferences, predictive accuracy can be significantly improved. However, previous methods typ ..."
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Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation can simplify and streamline the user experience, and by modeling user preferences, predictive accuracy can be significantly improved. However, previous methods typically model user behavior based only on a log of prior tags, neglecting other behaviors and information in social tagging systems, e.g., commenting on items and connecting with other users. On the other hand, little is known about the connection and correlations among these behaviors and contexts in social tagging systems. In this paper, we investigate improved modeling for predictive social tagging systems. Our explanatory analyses demonstrate three significant challenges: coupled high order interaction, data sparsity and cold start on items. We tackle these problems by using a generalized latent factor model and fully Bayesian treatment. To evaluate performance, we test on two real-world data sets from Flickr and Bibsonomy. Our experiments on these data sets show that to achieve best predictive performance, it is necessary to employ a fully Bayesian treatment in modeling high order relations in asocial tagging system. Ourmethods noticeably outperform state-of-the-art approaches.
Connecting Comments and Tags: Improved Modeling of Social Tagging Systems
"... Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation can simplify and streamline the user experience, and by modeling user preferences, predictive accuracy can be significantly improved. However, previous methods typ ..."
Abstract
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Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation can simplify and streamline the user experience, and by modeling user preferences, predictive accuracy can be significantly improved. However, previous methods typically model user behavior based only on a log of prior tags, neglecting other behaviors and information in social tagging systems, e.g., commenting on items and connecting with other users. On the other hand, little is known about the connection and correlations among these behaviors and contexts in social tagging systems. In this paper, we investigate improved modeling for predictive social tagging systems. Our explanatory analyses demonstrate three significant challenges: coupled high order interaction, data sparsity and cold start on items. We tackle these problems by using a generalized latent factor model and fully Bayesian treatment. To evaluate performance, we test on two real-world data sets from Flickr and Bibsonomy. Our experiments on these data sets show that to achieve best predictive performance, it is necessary to employ a fully Bayesian treatment in modeling high order relationsinasocialtaggingsystem. Ourmethodsnoticeably outperform state-of-the-art approaches.
