Deep Text Mining of Instagram Data Without Strong Supervision

With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This textual data can be analyzed for the purpose of improving user recommendations and detecting trends. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing Twitter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on annotated training data, which in practice is both difficult and expensive to obtain. In this paper, we present methods for unsupervised mining of fashion attributes from Instagram text, which can enable a new kind of user recommendation in the fashion domain. In this context, we analyze a corpora of Instagram posts from the fashion domain, introduce a system for extracting fashion attributes from Instagram, and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text. With our experiments, we confirm that word embeddings are a useful asset for information extraction. Experimental results show that information extraction using word embeddings outperforms a baseline that uses Levenshtein distance. The results also show the benefit of combining weak supervision signals using generative models instead of majority voting. Using weak supervision and generative modeling, an F1 score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance. Finally, our empirical study provides one of the few available studies on Instagram text and shows that the text is noisy, that the text distribution exhibits the long-tail phenomenon, and that comment sections on Instagram are multi-lingual.Comment: 8 pages, 5 figures. Pre-print for paper to appear in conference proceedings for the Web Intelligence Conferenc

Recommender Systems in Fashion 2019

This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be a very useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer. The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability

Trust-Based User Profiling

We have introduced the notion of user profiling with trust, as a solution to theproblem of uncertainty and unmanageable exposure of personal data duringaccess, retrieval and consumption by web applications. Our solution sug-gests explicit modeling of trust and embedding trust metrics and mechanismswithin very fabric of user profiles. This has in turn allowed information sys-tems to consume and understand this extra knowledge in order to improveinteraction and collaboration among individuals and system. When formaliz-ing such profiles, another challenge is to realize increasingly important notionof privacy preferences of users. Thus, the profiles are designed in a way toincorporate preferences of users allowing target systems to understand pri-vacy concerns of users during their interaction. A majority of contributionsof this work had impact on profiling and recommendation in digital librariescontext, and was implemented in the framework of EU FP7 Smartmuseumproject. Highlighted results start from modeling of adaptive user profilesincorporating users taste, trust and privacy preferences. This in turn led toproposal of several ontologies for user and content characteristics modeling forimproving indexing and retrieval of user content and profiles across the plat-form. Sparsity and uncertainty of profiles were studied through frameworksof data mining and machine learning of profile data taken from on-line so-cial networks. Results of mining and population of data from social networksalong with profile data increased the accuracy of intelligent suggestions madeby system to improving navigation of users in on-line and off-line museum in-terfaces. We also introduced several trust-based recommendation techniquesand frameworks capable of mining implicit and explicit trust across ratingsnetworks taken from social and opinion web. Resulting recommendation al-gorithms have shown to increase accuracy of profiles, through incorporationof knowledge of items and users and diffusing them along the trust networks.At the same time focusing on automated distributed management of profiles,we showed that coverage of system can be increased effectively, surpassingcomparable state of art techniques. We have clearly shown that trust clearlyelevates accuracy of suggestions predicted by system. To assure overall pri-vacy of such value-laden systems, privacy was given a direct focus when archi-tectures and metrics were proposed and shown that a joint optimal setting foraccuracy and perturbation techniques can maintain accurate output. Finally,focusing on hybrid models of web data and recommendations motivated usto study impact of trust in the context of topic-driven recommendation insocial and opinion media, which in turn helped us to show that leveragingcontent-driven and tie-strength networks can improve systems accuracy forseveral important web computing tasks.QC 20130219</p

Diversifying Product Review Rankings : Getting the Full Picture

E-commerce Web sites owe much of their popularityto consumer reviews provided together with product descriptions.On-line customers spend hours and hours going through heaps oftextual reviews to build confidence in products they are planningto buy. At the same time, popular products have thousands ofuser-generated reviews. Current approaches to present them tothe user or recommend an individual review for a product arebased on the helpfulness or usefulness of each review. In thispaper we look at the top-k reviews in a ranking to give a goodsummary to the user with each review complementing the others.To this end we use Latent Dirichlet Allocation to detect latenttopics within reviews and make use of the assigned star ratingfor the product as an indicator of the polarity expressed towardsthe product and the latent topics within the review. We present aframework to cover different ranking strategies based on theuser’s need: Summarizing all reviews; focus on a particularlatent topic; or focus on positive, negative or neutral aspects.We evaluated the system using manually annotated review datafrom a commercial review Web site.Winner of best paper award at 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 2012021

Diversifying Product Review Rankings : Getting the Full Picture

E-commerce Web sites owe much of their popularityto consumer reviews provided together with product descriptions.On-line customers spend hours and hours going through heaps oftextual reviews to build confidence in products they are planningto buy. At the same time, popular products have thousands ofuser-generated reviews. Current approaches to present them tothe user or recommend an individual review for a product arebased on the helpfulness or usefulness of each review. In thispaper we look at the top-k reviews in a ranking to give a goodsummary to the user with each review complementing the others.To this end we use Latent Dirichlet Allocation to detect latenttopics within reviews and make use of the assigned star ratingfor the product as an indicator of the polarity expressed towardsthe product and the latent topics within the review. We present aframework to cover different ranking strategies based on theuser’s need: Summarizing all reviews; focus on a particularlatent topic; or focus on positive, negative or neutral aspects.We evaluated the system using manually annotated review datafrom a commercial review Web site.Winner of best paper award at 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 2012021

Forging Trust and Privacy with User Modeling Frameworks : An Ontological Analysis

With the ever increasing importance of social net- working sites and services, socially intelligent agents who are responsible for gathering, managing and maintaining knowledge surrounding individual users are of increasing interest to both computing research communities as well as industries. For these agents to be able to fully capture and manage the knowledge about a user’s interaction with these social sites and services, a social user model needs to be introduced. A social user model is defined as a generic user model (model capable of capturing generic information related to a user), plus social dimensions of users (models capturing social aspects of user such as activities and social contexts). While existing models capture a proportion of such information, they fail to model and present ones of the most important dimensions of social connectivity: trust and privacy. To this end, in this paper, we introduce an ontological model of social user, composed by a generic user model component, which imports existing well-known user model structures, a social model, which contains social dimensions, and trust, reputation and privacy become the pivotal concepts gluing the whole ontological knowledge models together.QC 20120215</p

Semantics and Syntactic Use of Indeclinable Particle in -ot(-ies)

Bakalaura darba "Nelokāmā divdabja ar -ot(-ies) semantika un sintaktiskais lietojums" mērķis ir izpētīt šī divdabja semantiku un sintaktisko lietojumu publicistikā, īpašu uzmanību pievēršot sintaktiskajām konstrukcijām, kurās divdabis lietots. Tika ekscerpēts aptuveni 400 piemēru ar šī divdabja lietojumu. Darba teorētisko pamatu pārsvarā sastāda informācija no latviešu valodas gramatikām. Darbā veltīta viena nodaļa tam, lai aprakstītu visus atrastos veidus, kā nosaukts divdabja izteiktās darbības darītājs, un viena nodaļa iespējamajām sintaktiskajām attieksmēm, ko veido šis divdabis attiecībā pret teikumu. Sistematizēti iespējamie darītāja izteikšanas paņēmieni kā arī jēdzieniskās attieksmes, ko veido divdabis attiecībā pret pārējo teikumu.The aim of the research Semantics and Syntactic Use of Indeclinable Participle with -ot(-ies) is to explore the semantics and syntactic use of this indeclinable participle in language of mass media, focusing on syntactic constructions in which this participle is used. Were excerpted about 400 examples with use of this indeclinable participle. The theoretical material mostly is based on Latvian Grammars. One chapter describes all the researched possibilities of how the agent of this participle can function in clause, and one chapter describes all possible semantic relations between indeclinable participle and the rest of the sentence. All possibilities of agent and semantic relations of this indeclinable participle and the rest of the sentence are systematized

Elevating Prediction Accuracy in Trust-aware Collaborative Filtering Recommenders through T-index Metric and TopTrustee lists

The growing popularity of Social Networks raises the important issue of trust. Among many systems which have realized the impact of trust, Recommender Systems have been the most influential ones. Collaborative Filtering Recommenders take advantage of trust relations between users for generating more accurate predictions. In this paper, we propose a semantic recommendation framework for creating trust relationships among all types of users with respect to different types of items, which are accessed by unique URI across heterogeneous networks and environments. We gradually build up the trust relationships between users based on the rating information from user profiles and item profiles to generate trust networks of users. For analyzing the formation of trust networks, we employ T-index as an estimate of a user’s trustworthiness to identify and select neighbors in an effective manner. In this work, we utilize T-index to form the list of an item’s raters, called Top- Trustee list for keeping the most reliable users who have already shown interest in the respective item. Thus, when a user rates an item, he/she is able to find users who can be trustworthy neighbors even though they might not be accessible within an upper bound of traversal path length. An empirical evaluation demonstrates how T-index improves the Trust Network structure by generating connections to more trustworthy users. We also show that exploiting Tindex results in better prediction accuracy and coverage of recommendations collected along few edges that connect users on a Social Network

Trust and Privacy Enabled Service Composition using Social Experience

In this paper, we present a framework for automatic selection andcomposition of services which exploits trustworthiness of services as a metric formeasuring the quality of service composition. Trustworthiness is defined in terms ofservice reputation extracted from user profiles. The profiles are, in particular, extractedand inferred from a social network which accumulates users past experience withcorresponding services. Using our privacy inference model we, first, prune socialnetwork to hide privacy sensitive contents and, then, utilize a trust inference basedalgorithm to measure reputation score of each individual service, and subsequentlytrustworthiness of their compositionQC 20120206</p

Trust and privacy correlations in social networks: A deep learning framework

Online Social Networks (OSNs) remain the focal point of Internet usage. Since the beginning, networking sites tried best to have right privacy mechanisms in place for users, enabling them to share the right content with the right audience. With all these efforts, privacy customizations remain hard for users across the sites. Existing research that address this problem mainly focus on semi-supervised strategies that introduce extra complexity by requiring the user to manually specify initial privacy preferences for their friends. In this work, we suggest an adaptive solution that can dynamically generate privacy labels for users in OSNs. To this end, we introduce a deep reinforcement learning framework that targets two key problems in OSNs like Facebook: the exposure of users' interactions through the network to less trusted direct friends, and the possibility of propagating user updates through direct friends' interactions to indirect friends. By implementing this framework, we aim at understanding how social trust and privacy could be correlated, specifically in a dynamic fashion. We report the ranked dependence between the generated privacy labels and the estimated user trust values, which indicate the ability of the framework to identify the highly trusted users and share with them higher percentages of data