6,109 research outputs found
Tweeting your Destiny: Profiling Users in the Twitter Landscape around an Online Game
Social media has become a major communication channel for communities
centered around video games. Consequently, social media offers a rich data
source to study online communities and the discussions evolving around games.
Towards this end, we explore a large-scale dataset consisting of over 1 million
tweets related to the online multiplayer shooter Destiny and spanning a time
period of about 14 months using unsupervised clustering and topic modelling.
Furthermore, we correlate Twitter activity of over 3,000 players with their
playtime. Our results contribute to the understanding of online player
communities by identifying distinct player groups with respect to their Twitter
characteristics, describing subgroups within the Destiny community, and
uncovering broad topics of community interest.Comment: Accepted at IEEE Conference on Games 201
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings
In this paper we present a novel interactive multimodal learning system,
which facilitates search and exploration in large networks of social multimedia
users. It allows the analyst to identify and select users of interest, and to
find similar users in an interactive learning setting. Our approach is based on
novel multimodal representations of users, words and concepts, which we
simultaneously learn by deploying a general-purpose neural embedding model. We
show these representations to be useful not only for categorizing users, but
also for automatically generating user and community profiles. Inspired by
traditional summarization approaches, we create the profiles by selecting
diverse and representative content from all available modalities, i.e. the
text, image and user modality. The usefulness of the approach is evaluated
using artificial actors, which simulate user behavior in a relevance feedback
scenario. Multiple experiments were conducted in order to evaluate the quality
of our multimodal representations, to compare different embedding strategies,
and to determine the importance of different modalities. We demonstrate the
capabilities of the proposed approach on two different multimedia collections
originating from the violent online extremism forum Stormfront and the
microblogging platform Twitter, which are particularly interesting due to the
high semantic level of the discussions they feature
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
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Dynamic User Profiling for Search Personalisation
The performance of a personalised search system largely depends upon the ability to build user profiles which accurately capture the user's search interests. However, many approaches to user profiling have neglected the dynamic nature of the user's search interests. That is, a user's search interests typically change in response to their interactions with the search system during the search period. Therefore, a profile built for previous searches might not reflect that user's current search interests.
A widely used type of profile represents the topical interests of the user. In these cases, a typical approach is to build a user profile using topics discussed in documents which the user has found relevant, and where the topics are obtained from a human-generated ontology or directory. However, a key limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the human-generated ontology requires manual effort to determine the correct categories for each document.
In this research, we address these problems by proposing novel techniques for dynamically building user profiles which capture the user's search interests changing over time. Instead of using a human-generated ontology, we use a topic modelling technique (Latent Dirichlet Allocation) for unsupervised extraction of the topics from documents. To dynamically build user profiles, we make two important assumptions. First, that the group of users with whom a user shares a set of common interests may be different depending upon the particular topic of interest. Second, the more recently clicked/relevant documents tell us more about the user's current search interests.
To test these assumptions, we develop and implement dynamic user profiles, and then evaluate them on two search personalisation tasks. Our first chosen task is personalising search results returned by a Web search engine, and the second is the task of personalising query suggestions made by an Intranet search engine. We found that dynamic user profiles can significantly improve the ranking quality over well-established baselines
Task-based user profiling for query refinement (toque)
The information needs of search engine users vary in complexity. Some simple needs can be satisfied by using a single query, while complicated ones require a series of queries spanning a period of time. A search task, consisting of a sequence of search queries serving the same information need, can be treated as an atomic unit for modeling user’s search preferences and has been applied in improving the accuracy of search results. However, existing studies on user search tasks mainly focus on applying user’s interests in re-ranking search results. Only few studies have examined the effects of utilizing search tasks to assist users in obtaining effective queries. Moreover, fewer existing studies have examined the dynamic characteristics of user’s search interests within a search task. Furthermore, even fewer studies have examined approaches to selective personalization for candidate refined queries that are expected to benefit from its application. This study proposes a framework of modeling user’s task-based dynamic search interests to address these issues and makes the following contributions. First, task identification: a cross-session based method is proposed to discover tasks by modeling the best-link structure of queries, based on the commonly shared clicked results. A graph-based representation method is introduced to improve the effectiveness of link prediction in a query sequence. Second, dynamic task-level search interest representation: a four-tuple user profiling model is introduced to represent long- and short-term user interests extracted from search tasks and sessions. It models user’s interests at the task level to re-rank candidate queries through modules of task identification and update. Third, selective personalization: a two-step personalization algorithm is proposed to improve the rankings of candidate queries for query refinement by assessing the task dependency via exploiting a latent task space. Experimental results show that the proposed TOQUE framework contributes to an increased precision of candidate queries and thus shortened search sessions
Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context
An important way to improve users’ satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users’ history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource—Delicious bookmark—to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data
UnifiedSSR: A Unified Framework of Sequential Search and Recommendation
In this work, we propose a Unified framework of Sequential Search and
Recommendation (UnifiedSSR) for joint learning of user behavior history in both
search and recommendation scenarios. Specifically, we consider user-interacted
products in the recommendation scenario, user-interacted products and
user-issued queries in the search scenario as three distinct types of user
behaviors. We propose a dual-branch network to encode the pair of interacted
product history and issued query history in the search scenario in parallel.
This allows for cross-scenario modeling by deactivating the query branch for
the recommendation scenario. Through the parameter sharing between dual
branches, as well as between product branches in two scenarios, we incorporate
cross-view and cross-scenario associations of user behaviors, providing a
comprehensive understanding of user behavior patterns. To further enhance user
behavior modeling by capturing the underlying dynamic intent, an
Intent-oriented Session Modeling module is designed for inferring
intent-oriented semantic sessions from the contextual information in behavior
sequences. In particular, we consider self-supervised learning signals from two
perspectives for intent-oriented semantic session locating, which encourage
session discrimination within each behavior sequence and session alignment
between dual behavior sequences. Extensive experiments on three public datasets
demonstrate that UnifiedSSR consistently outperforms state-of-the-art methods
for both search and recommendation
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