445 research outputs found

    Large Scale Visual Recommendations From Street Fashion Images

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    We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose four data driven models in the form of Complementary Nearest Neighbor Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain LDA for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. Finally, we also outline a large-scale annotated data set of fashion images (Fashion-136K) that can be exploited for future vision research

    Explainable online recommendation systems with self-identity theory and attribute learning method

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    In recent years, Online Shopping plays an important role in daily life and how to improve the online shopping experience with Machine Learning and Recommender System has been discussed by a group of researchers. As a sub-field of Machine Learning, Computer vision has achieved significant developments during the last decade. The computer vision techniques can help machine to view images and extract useful information from images like human beings. However, the existing online recommender system has mostly used the labelled information and ignored the large amount of useful information extracted from images. This thesis proposed that the extracted information from images through computer vision techniques can be used in the current online recommender system for the improved online shopping experience. To do this, I firstly tackled the problem of insufficient data in the real online shopping environment. I proposed a pairwise constraint random forest algorithm with associating transfer learning strategy. This new algorithm can make use of weakly supervised labelled data which is relatively easy to collect in the real online shopping environment to train the attribute classification model. Secondly, I developed an explainable recommender system with self-identity theory. This new recommender framework is built based on the weakly learning algorithm proposed above to analyse human behaviours by self-identity theory from information system research. Compared with previous recommender system, my work concentrates on different customer behaviours distinguished by self-identity and result in an improved online shopping experience. In summary, there are two major contributes for this thesis. Firstly, this thesis introduces a new weakly-supervised learning approach for semantic data classification in the online shopping environment. This new algorithm can work with noise partially labelled data to achieve better accuracy for attribute learning tasks. Secondly, by analysing the recommender system with self-identity theory, a new explainable Recommender System is proposed to improve online shopping experience. Besides, we also indicate the potential of further research in combining Computer Vision in Computer Science with online shopping experience in Information System research which can determine how Computer Vision can help to solve real world problems

    Apparel recommendation system evolution: an empirical review

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    Purpose: With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers' economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market. Design/methodology/approach: This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords. Findings: This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors' research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system. Originality/value: Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective

    Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation

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    Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based recommendation, which utilizes customer session data to predict their next interaction, has become increasingly popular. However, existing session datasets have limitations in terms of item attributes, user diversity, and dataset scale. As a result, they cannot comprehensively capture the spectrum of user behaviors and preferences. To bridge this gap, we present the Amazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales, where the major languages of products are English, German, Japanese, French, Italian, and Spanish. Remarkably, the dataset can help us enhance personalization and understanding of user preferences, which can benefit various existing tasks as well as enable new tasks. To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation. With the above tasks, we benchmark a range of algorithms on our proposed dataset, drawing new insights for further research and practice. In addition, based on the proposed dataset and tasks, we hosted a competition in the KDD CUP 2023 and have attracted thousands of users and submissions. The winning solutions and the associated workshop can be accessed at our website https://kddcup23.github.io/.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmarks; Dataset for KDD Cup 2023, https://kddcup23.github.io

    A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

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    E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations

    Uutisten välityksen adaptiivinen suositusjärjestelmä

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    Modern news websites contain plenty of constantly changing content. To better cater for different user types, the content shown needs to be personalised. This thesis presents the development of a recommender system for a large media company. The recommender system generates suggestions for news articles, and the personalised content is to replace the company's news site's current manually composed front page. The main goal is to increase user activity on the news site. One of the challenges in creating a news recommender system is the vast amount of user and article data. Furthermore, the relevance of articles usually changes over time. As a result, there is a need for consideration of the cold-start problem and fast adaptability. Consequently, the chosen approach is memory-based. In this thesis, the preferences of each user are modelled by the users' visit frequency and the sections they read articles from. The recommender system can therefore be classified as content-based, with some context-based additions. As an addition, article importance scores are formed with a novel approach and are used as a basis for calculating scores for articles. They reflect the editorial view of articles and aid in maintaining the general feel of the news site. The resulting recommender system is fast, lightweight, and can provide suggestions even with very little user transaction data. However, the results of the chosen performance metrics are inconclusive: for all tested parameter variants, some of the metrics show an increase in user activity while others show the opposite. The proposed next steps are to either do more online testing with different parameter combinations or to implement new features.Modernit uutissivustot sisältävät runsaasti jatkuvasti muuttuvaa sisältöä. Erilaisten käyttäjien tarpeisiin vastataan paremmin personoimalla näytetty sisältö. Diplomityössä kehitetään suositusjärjestelmä suurelle mediayhtiölle. Suositusjärjestelmä ehdottaa uutisartikkeleita käyttäjille. Nämä ehdotukset tulevat korvaamaan yhtiön uutissivuston nykyisen etusivun. Päätavoiteena on kasvattaa sivuston käyttöä. Yksi uutisten suosittelujärjestelmän luomiseen liittyvistä haasteista on käyttäjä- ja artikkelidatan suuri määrä. Lisäksi artikkelien merkityksellisyys muuttuu yleensä ajan myötä. Tämän takia erityisesti kylmäkäynnistys-ongelmaan sekä nopeaan adaptiivisuuteen on kiinnitettävä huomiota. Järjestelmään on siksi valittu muistipohjainen lähestymistapa. Diplomityössä käyttäjien mieltymyksiä mallinnetaan heidän käyttöuseuden sekä luettujen artikkelien osioiden avulla. Suosittelujärjestelmää voidaan siten kutsua sisältöpohjaiseksi, kontekstipohjaisin lisäyksin. Käyttäjäprofiilin lisäksi suositusten muodostamista varten luodaan uudenlainen artikkelin tärkeys -arvo. Se heijastelee toimituksen näkemyksiä ja auttaa ylläpitämään sivuston tuntuman. Luotu suositusjärjestelmä on nopea, kevyt ja tekee suositteluja jo hyvin pienellä määrällä dataa. Valituilla mittareilla saatujen tulosten perusteella ei kuitenkaan pystytä kiistatta sanomaan, että sivuston käyttö kasvaisi. Kaikilla kokeilluilla parametrivaihtoehdoilla mittareista osan mukaan käyttö kasvaa ja toisten mukaan vähenee. Seuraaviksi kehityskohteiksi ehdotetaan lisää käyttäjätestejä tai uusien ominaisuuksien kehittämistä parempien tulosten tavoittelemiseksi

    Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding

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    Several techniques to map various types of components, such as words, attributes, and images, into the embedded space have been studied. Most of them estimate the embedded representation of target entity as a point in the projective space. Some models, such as Word2Gauss, assume a probability distribution behind the embedded representation, which enables the spread or variance of the meaning of embedded target components to be captured and considered in more detail. We examine the method of estimating embedded representations as probability distributions for the interpretation of fashion-specific abstract and difficult-to-understand terms. Terms, such as "casual," "adult-casual,'' "beauty-casual," and "formal," are extremely subjective and abstract and are difficult for both experts and non-experts to understand, which discourages users from trying new fashion. We propose an end-to-end model called dual Gaussian visual-semantic embedding, which maps images and attributes in the same projective space and enables the interpretation of the meaning of these terms by its broad applications. We demonstrate the effectiveness of the proposed method through multifaceted experiments involving image and attribute mapping, image retrieval and re-ordering techniques, and a detailed theoretical/analytical discussion of the distance measure included in the loss function

    Human-Understandable Explanations of Neural Networks

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    Das 21. Jahrhundert ist durch Datenströme enormen Ausmaßes gekennzeichnet. Dies hat die Popularität von Berechnungsmodellen, die sehr datenintensiv sind, wie z.B. neuronale Netze, drastisch erhöht. Aufgrund ihres großen Erfolges bei der Mustererkennung sind sie zu einem leistungsstarken Werkzeug für Vorhersagen, Klassifizierung und Empfehlungen in der Informatik, Statistik, Wirtschaft und vielen anderen Disziplinen geworden. Trotz dieser verbreiteten Anwendung sind neuronale Netze Blackbox-Modelle, d.h. sie geben keine leicht interpretierbaren Einblicke in die Struktur der approximierten Funktion oder in die Art und Weise, wie die Eingabe in die entsprechende Ausgabe umgewandelt wird. Die jüngste Forschung versucht, diese Blackboxen zu öffnen und ihr Innenleben zu enthüllen. Bisher haben sich die meisten Forschungsarbeiten darauf konzentriert, die Entscheidungen eines neuronalen Netzes auf einer sehr technischen Ebene und für ein Informatikfachpublikum zu erklären. Da neuronale Netze immer häufiger eingesetzt werden, auch von Menschen ohne tiefere Informatikkenntnisse, ist es von entscheidender Bedeutung, Ansätze zu entwickeln, die es ermöglichen, neuronale Netze auch für Nicht-Experten verständlich zu erklären. Das Ziel ist, dass Menschen verstehen können, warum das neuronale Netz bestimmte Entscheidungen getroffen hat, und dass sie das Ergebnis des Modells durchgehend interpretieren können. Diese Arbeit beschreibt ein Rahmenwerk, das es ermöglicht, menschlich verständliche Erklärungen für neuronale Netze zu liefern. Wir charakterisieren menschlich nachvollziehbare Erklärungen durch sieben Eigenschaften, nämlich Transparenz, Überprüfbarkeit, Vertrauen, Effektivität, Überzeugungskraft, Effizienz und Zufriedenheit. In dieser Arbeit stellen wir Erklärungsansätze vor, die diese Eigenschaften erfüllen. Zunächst stellen wir TransPer vor, ein Erklärungsrahmenwerk für neuronale Netze, insbesondere für solche, die in Produktempfehlungssystemen verwendet werden. Wir definieren Erklärungsmaße auf der Grundlage der Relevanz der Eingaben, um die Vorhersagequalität des neuronalen Netzes zu analysieren und KI-Anwendern bei der Verbesserung ihrer neuronalen Netze zu helfen. Dadurch werden Transparenz und Vertrauen geschaffen. In einem Anwendungsfall für ein Empfehlungssystem werden auch die Überzeugungskraft, die den Benutzer zum Kauf eines Produkts veranlasst, und die Zufriedenheit, die das Benutzererlebnis angenehmer macht, berücksichtigt. Zweitens, um die Blackbox des neuronalen Netzes zu öffnen, definieren wir eine neue Metrik für die Erklärungsqualität ObAlEx in der Bildklassifikation. Mit Hilfe von Objekterkennungsansätzen, Erklärungsansätzen und ObAlEx quantifizieren wir den Fokus von faltenden neuronalen Netzwerken auf die tatsächliche Evidenz. Dies bietet den Nutzern eine effektive Erklärung und Vertrauen, dass das Modell seine Klassifizierungsentscheidung tatsächlich auf der Grundlage des richtigen Teils des Eingabebildes getroffen hat. Darüber hinaus ermöglicht es die Überprüfbarkeit, d. h. die Möglichkeit für den Benutzer, dem Erklärungssystem mitzuteilen, dass sich das Modell auf die falschen Teile des Eingabebildes konzentriert hat. Drittens schlagen wir FilTag vor, einen Ansatz zur Erklärung von faltenden neuronalen Netzwerken durch die Kennzeichnung der Filter mit Schlüsselwörtern, die Bildklassen identifizieren. In ihrer Gesamtheit erklären diese Kennzeichnungen die Zweckbestimmung des Filters. Einzelne Bildklassifizierungen können dann intuitiv anhand der Kennzeichnungen der Filter, die das Eingabebild aktiviert, erklärt werden. Diese Erklärungen erhöhen die Überprüfbarkeit und das Vertrauen. Schließlich stellen wir FAIRnets vor, das darauf abzielt, Metadaten von neuronalen Netzen wie Architekturinformationen und Verwendungszweck bereitzustellen. Indem erklärt wird, wie das neuronale Netz aufgebaut ist werden neuronale Netzer transparenter; dadurch dass ein Nutzer schnell entscheiden kann, ob das neuronale Netz für den gewünschten Anwendungsfall relevant ist werden neuronale Netze effizienter. Alle vier Ansätze befassen sich mit der Frage, wie man Erklärungen von neuronalen Netzen für Nicht-Experten bereitstellen kann. Zusammen stellen sie einen wichtigen Schritt in Richtung einer für den Menschen verständlichen KI dar
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