User-generated item lists are a popular feature of many different platforms. Examples include lists of books on Goodreads, playlists on Spotify and YouTube, collections of images on Pinterest, and lists of answers on question-answer sites like Zhihu. Recommending item lists is critical for increasing user engagement and connecting users to new items, but many approaches are designed for the item-based recommendation, without careful consideration of the complex relationships between items and lists. Hence, in this paper, we propose a novel user-generated list recommendation model called AttList. Two unique features of AttList are careful modeling of (i) hierarchical user preference, which aggregates items to characterize the list that they belong to, and then aggregates these lists to estimate the user preference, naturally fitting into the hierarchical structure of item lists; and (ii) item and list consistency, through a novel self-attentive aggregation layer designed for capturing the consistency of neighboring items and lists to better model user preference. Through experiments over three real-world datasets reflecting different kinds of user-generated item lists, we find that AttList results in significant improvements in NDCG, [email protected], and [email protected] versus a suite of state-of-the-art baselines. Furthermore, all code and data are available at https://github.com/heyunh2015/AttList.

In this paper, we investigate the impact of spatial variation on the construction of location-sensitive user profiles. We demonstrate evidence of spatial variation over a collection of Twitter Lists, wherein we find that crowdsourced labels are constrained by distance. For example, that energy in San Francisco is more associated with the green movement, whereas in Houston it is more associated with oil and gas. We propose a three-step framework for location-sensitive user profiling: first, it constructs a crowdsourced label similarity graph, where each labeler and labelee are annotated with a geographic coordinate; second, it transforms this similarity graph into a directed weighted tree that imposes a hierarchical structure over these labels; third, it embeds this location-sensitive folksonomy into a user profile ranking algorithm that outputs a ranked list of candidate labels for a partially observed user profile. Through extensive experiments over a Twitter list dataset, we demonstrate the effectiveness of this location-sensitive user profiling.

Complementary item recommendation finds products that go well with one another (e.g., a camera and a specific lens). While complementary items are ubiquitous, the dimensions by which items go together can vary by both product and category, making it difficult to detect complementary items at scale. Moreover, in practice, user preferences for complementary items can be complex combinations of item quality and evidence of complementarity. Hence, we propose a new neural complementary recommender Encore that can jointly learn complementary item relationships and user preferences. Specifically, Encore (i) effectively combines and balances both stylistic and functional evidence of complementary items across item categories; (ii) naturally models item latent quality for complementary items through Bayesian inference of customer ratings; and (iii) builds a novel neural network model to learn the complex (non-linear) relationships between items for flexible and scalable complementary product recommendations. Through experiments over large Amazon datasets, we find that Encore effectively learns complementary item relationships, leading to an improvement in accuracy of 15.5% on average versus the next-best alternative.

Understanding user interests and expertise is a vital component toward creating rich user models for information personalization in social media, recommender systems and web search. To capture the pair-wise interactions between geo-location and user's topical profile in social-spatial systems, we propose the modeling of fine-grained and multi-dimensional user geo-topic profiles. We then propose a two-layered Bayesian hierarchical user factorization generative framework to overcome user heterogeneity and another enhanced model integrated with user's contextual information to alleviate multi-dimensional sparsity. Through extensive experiments, we find the proposed model leads to a 5\textasciitilde13% improvement in precision and recall over the alternative baselines and an additional 6\textasciitilde11% improvement with the integration of user's contexts.

We propose a new model toward improving the quality of image recommendations in social sharing communities like Pinterest, Flickr, and Instagram. Concretely, we propose Neural Personalized Ranking (NPR) -- a personalized pairwise ranking model over implicit feedback datasets -- that is inspired by Bayesian Personalized Ranking (BPR) and recent advances in neural networks. We further build an enhanced model by augmenting the basic NPR model with multiple contextual preference clues including user tags, geographic features, and visual factors. In our experiments over the Flickr YFCC100M dataset, we demonstrate the proposed NPR model is more effective than multiple baselines. Moreover, the contextual enhanced NPR model significantly outperforms the base model by 16.6% and a contextual enhanced BPR model by 4.5% in precision and recall.

With the rapid adoption of smartphones worldwide and the reliance on app marketplaces to discover new apps, these marketplaces are critical for connecting users with apps. And yet, the user reviews and ratings on these marketplaces may be strategically targeted by app developers. We investigate the use of crowdsourcing platforms to manipulate app reviews. We find that (i) apps targeted by crowdsourcing platforms are rated significantly higher on average than other apps; (ii) the reviews themselves arrive in bursts; (iii) app reviewers tend to repeat themselves by relying on some standard repeated text; and (iv) apps by the same developer tend to share a more similar language model: if one app has been targeted, it is likely that many of the other apps from the same developer have also been targeted.

This paper tackles the geospatial tag estimation problem, which is of critical importance for location-based search, retrieval, and mining applications. However, tag estimation is challenging due to massive sparsity, uncertainty in the tags actually used, as well as diversity across locations and times. Toward overcoming these challenges, we propose a community-based smoothing approach that seeks to uncover hidden conceptual communities which link multiple related locations by their common interests in addition to their proximity. Through extensive experiments over a sample of millions of geotagged Twitter posts, we demonstrate the effectiveness of the smoothing approach and validate the intuition that geo-locations have the tendency to share similar “ideas” in the formation of conceptual communities.

Discovering what people are known for is valuable to many important applications such as recommender systems. Unlike an individual's personal interests, what a user is known for is reflected by the views of others, and is often not easily discerned for a long-tail of the vast majority of users. In this paper, we tackle the problem of discovering what users are known for through a probabilistic model called Bayesian Contextual Poisson Factorization. Moving beyond just modeling user's content, it naturally models and integrates additional contextual factors, concretely, user's geo-spatial footprints and social influence, to overcome noisy online activities and social relations. Through GPS-tagged social media datasets, we find that the proposed method can improve known-for prediction performance by 17.5% in precision and 20.9% in recall on average, and that it can capture the implicit relationships between a user's known-for profile and her content, geo-spatial and social influence.

We propose a lightweight system for (i) semi-automatically discovering and tracking bias themes associated with opposing sides of a topic; (ii) identifying strong partisans who drive the online discussion; and (iii) inferring the opinion bias of "regular" participants. By taking just two hand-picked seeds to characterize the topic-space (e.g., "pro-choice" and "pro-life") as weak labels, we develop an efficient optimization-based opinion bias propagation method over the social/information network. We show how this approach leads to a 20% accuracy improvement versus a next-best alternative for bias estimation, as well as uncovering the opinion leaders and evolving themes associated with these topics. We also demonstrate how the inferred opinion bias can be integrated into user recommendation, leading to a 26% improvement in precision.