2,128 research outputs found
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in
recommender systems in industry, owing to its strength in user interest
modeling and ease in online personalization. By constructing a user's profile
with the items that the user has consumed, ICF recommends items that are
similar to the user's profile. With the prevalence of machine learning in
recent years, significant processes have been made for ICF by learning item
similarity (or representation) from data. Nevertheless, we argue that most
existing works have only considered linear and shallow relationship between
items, which are insufficient to capture the complicated decision-making
process of users.
In this work, we propose a more expressive ICF solution by accounting for the
nonlinear and higher-order relationship among items. Going beyond modeling only
the second-order interaction (e.g. similarity) between two items, we
additionally consider the interaction among all interacted item pairs by using
nonlinear neural networks. Through this way, we can effectively model the
higher-order relationship among items, capturing more complicated effects in
user decision-making. For example, it can differentiate which historical
itemsets in a user's profile are more important in affecting the user to make a
purchase decision on an item. We treat this solution as a deep variant of ICF,
thus term it as DeepICF. To justify our proposal, we perform empirical studies
on two public datasets from MovieLens and Pinterest. Extensive experiments
verify the highly positive effect of higher-order item interaction modeling
with nonlinear neural networks. Moreover, we demonstrate that by more
fine-grained second-order interaction modeling with attention network, the
performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
Traditional recommendation systems are faced with two long-standing
obstacles, namely, data sparsity and cold-start problems, which promote the
emergence and development of Cross-Domain Recommendation (CDR). The core idea
of CDR is to leverage information collected from other domains to alleviate the
two problems in one domain. Over the last decade, many efforts have been
engaged for cross-domain recommendation. Recently, with the development of deep
learning and neural networks, a large number of methods have emerged. However,
there is a limited number of systematic surveys on CDR, especially regarding
the latest proposed methods as well as the recommendation scenarios and
recommendation tasks they address. In this survey paper, we first proposed a
two-level taxonomy of cross-domain recommendation which classifies different
recommendation scenarios and recommendation tasks. We then introduce and
summarize existing cross-domain recommendation approaches under different
recommendation scenarios in a structured manner. We also organize datasets
commonly used. We conclude this survey by providing several potential research
directions about this field
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Link formation in mobile and economic networks : model and empirical analysis
In this dissertation, we study three link formation problems in mobile and economic networks: (i) company matching for mergers and acquisitions (M&A) network in the high-technology (high-tech) industry, (ii) mobile application (app) matching for cross promotion network in mobile app markets, and (iii) online friendship formation in mobile social networks. Each problem can be modeled as link formation problem in a graph, where nodes represent independent entities (e.g., companies, apps, users) and edges represent interactions (e.g., transactions, promotions, friendships) among the nodes. First, we propose a new data-analytic approach to measure firms' dyadic business proximity to analyze M&A network in the high-tech industry. Specifically, our method analyzes the unstructured texts that describe firms' businesses using latent Dirichlet allocation (LDA) topic modeling, and constructs a novel business proximity measure based on the output. Using CrunchBase data including 24,382 high-tech companies and 1,689 M&A transactions, we empirically validate our business proximity measure in the context of industry intelligence and show the measure's effectiveness in an application of M&A network analysis. Based on the research, we build a cloud-based information system to facilitate competitive intelligence on the high-tech industry. Second, we analyze mobile app matching for cross promotion network in mobile app markets. Cross promotion (CP) is a new app promotion framework, in which a mobile app is promoted to the users of another app. Using IGAWorks data covering 1,011 CP campaigns, 325 apps, and 301,183 users, we evaluate the effectiveness of CP campaigns in comparison with existing ad channels such as mobile display ads. While CP campaigns, on average, are still suboptimal as compared with display ads, we find evidence that a careful matching of mobile apps can significantly improve the effectiveness of CP campaigns. Our empirical results show that app similarity, measured by LDA from apps' text descriptions, is a significant factor that increases the user engagement in CP campaigns. With this observation, we propose an app matching mechanism for the CP network to improve the ad effectiveness. Third, we study friendship network formation in a location-based social network. We build a structural model of social link creation that incorporates individual characteristics and pairwise user similarities. Specifically, we define four user proximity measures from biography, geography, mobility, and short messages (i.e., tweets). To construct proximity from unstructured text information, we build LDA topic models of user biography texts and tweets. Using Gowalla data with 385,306 users, three million locations, and 35 million check-in records, we empirically estimate the structural model to find evidence on the homophily effect in network formation.Computer Science
Harnessing the power of the general public for crowdsourced business intelligence: a survey
International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI
Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review
© 2013 IEEE. The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented
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