Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Consumer Purchase Behavior of Online Professional Sports Merchandise

The purpose of this study was to find out whether shoppers browsed online then purchased in a store or purchased online. There had been limited empirical research and knowledge about online shopping behavior in relation to professional sports merchandise. Therefore, this study looked at consumer behavior and the use of websites to purchase professional sports merchandise. Most of all when research was analyzed the author found some shortcomings that overlooked buyers who researched products online, and then purchased in stores. To achieve the goal of this study, a survey was administered to 100 undergraduate and graduate students from Concordia University, Saint Paul in the United States. It was discovered that 51% of shoppers browsed online, researched the product, read reviews, and then purchased at a brick and mortar store. Recommendations on future studies of professional sports apparel would be beneficial seeing as 94% of consumers purchase professional sports apparel, based on results from the survey that was administered. A second recommendation would be to uncover whether shoppers used a hand held device such as a cellphone or tablet to research, compare prices, or read reviews in a store before the consumer purchased merchandise

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

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

SOCIAL PRESENCE, TRUST, AND SOCIAL COMMERCE PURCHASE INTENTION: AN EMPIRICAL RESEARCH

Lacking the presence of human and social elements is claimed one major weakness that is hindering the growth of e-commerce. The emergence of social commerce (SC) might help ameliorate this situation. Social commerce is a new evolution of e-commerce that combines the commercial and social activities by deploying social technologies into e-commerce sites. Social commerce reintroduces the social aspect of shopping to e-commerce, increasing the degree of social presences in online environment. Drawing upon the social presence theory, this study theorizes the nature of social aspect in online SC marketplace by proposing a set of three social presence variables. These variables are then hypothesized to have positive impacts on trusting beliefs which in turn result in online purchase behaviors. The research model is examined via data collected from a typical ecommerce site in China. Our findings suggest that social presence factors grounded in social technologies contribute significantly to the building of the trustworthy online exchanging relationships. In doing so, this paper confirms the positive role of social aspect in shaping online purchase behaviors, providing a theoretical evidence for the fusion of social and commercial activities. Finally, this paper introduces a new perspective of e-commerce and calls more attention to this new phenomenon

Online Store Atmospherics: Development of a Gender-Neutral Measure

The study was designed to explore store atmospheric cues currently employed by online retailers. The specific research goals were to establish a comprehensive taxonomy of online store atmospheric cues; and to develop a gender-neutral measurement of online store atmospheric cues identified through qualitative and quantitative approaches. The study generated an initial item pool via literature review and a focus group interview, and personal interviews were conducted to identify possible online store atmospheric cues and classify items into the identified online store atmospheric cues. A pretest (n = 192) was conducted to initially purify items, and the main study (n = 1751) was conducted for measurement purification and validation. To generate a gender-neutral measurement, the differential item functioning test was conducted for every identified atmospheric cue to eliminate items showing biased responses between males and females. As a result, the study established a gender-neutral measurement consisting 52 items that measure 16 online store atmospheric cues: customization, font, layout, visual, rich media, content, CSR, order fulfillment, company information, merchandise, navigation, promotion, security, support, personalization, and social cues. The study provided implications to future researchers and online retailers based on the findings

Mobile shopping experience : The factors and emotions affecting the shopping experience on a smartphone

With respect to their usage, online stores’ conversion rates on smartphones are still lower than on computers or desktops. Even though smartphones have their device limitations, there is still a monetary gap which could be diminished by knowing how to properly design an online shopping experience for a smartphone. This study adopts the idea behind Stimulus-Organism-Response model. Based on this model, an online store contains stimuli, which affect the customer’s organism. The organism can be divided to cognition and affect. The customer’s conscious behaviour is due to their cognition and unconscious behaviour due to affect, in other words emotions. Different emotions cause different behavioural responses and by knowing which emotions are the most significant in customers’ online shopping experience, customers’ actions can be predicted. This study’s purpose is to find out which stimuli, or factors, are most significant in m-shopping context and what emotions these factors evoke. To find out what these significant factors and emotions are, an experiment including three shopping tests was conducted. The shopping tests were carried out in ethical fashion apparel online stores. The experiment was done by ten participants. From these results was formed a list of 43 factors, which hold significance in customer’s online shopping experience. These 43 factors were divided in six main categories, which are named as content, navigation, visual, product, smartphone, and environment and internal state categories. The significant emotions inside each category were determined with the help of an emotion assessment tool called Geneva Emotion Wheel. Based on this study’s results, the navigation category is the most significant category for shopping experience, and the product category for shopping outcome. The shopping experience is not only affected by the online store’s design, but also by the online store’s products, environment, customer’s internal state and customer’s smartphone, though smartphone’s significance is small. The navigation and content factors evoke similar emotions and are relevant for completing a shopping task. The visual and product categories for their part evoke similar emotions and are important for attracting the customer. The taxonomy of significant factors and emotions provided in this study can be used by both researchers and managers alike to further study and plan an online shopping experience.Vaikka älypuhelinten merkitys nyky-yhteiskunnassa on suuri, jää verkkokauppojen konversiot älypuhelimilla selatessa suhteessa tietokoneita pienemmiksi. Vaikka älypuhelimilla on omat rajoittavat tekijänsä, hyvän shoppailukokemuksen luomalla verkkokaupan konversiota älypuhelimella voitaisiin parantaa. Tämä tutkimus käyttää pohjanaan S-O-R mallia. Mallin mukaan verkkokauppa sisältää ärsykkeitä, mitkä vaikuttavat elimistöömme joko kognitiivisella- tai tunnetasolla. Asiakkaan tietoinen toiminta johtuu kognitiosta ja tiedostamaton toiminta puolestaan tunteista. Eri tunteet aiheuttavat erilaista käyttäytymistä, ja tietämällä mitkä tunteista ovat kaikkein merkittävimpiä asiakkaan shoppailukokemuksessa, yrityksen on mahdollista ennustaa asiakkaan käyttäytymistä. Tämän tutkimuksen tarkoitus on selvittää, mitkä ärsykkeet, toisin sanoen tekijät, ovat kaikkein merkittävimpiä mobiilikaupankäynnin kontekstissa ja mitä tunteita nämä tekijät herättävät. Tutkimusta varten luotiin koeasetelma, jossa toteutettiin shoppailutesti kolmessa eri eettisessä vaateverkkokaupassa. Koeasetelmaan osallistui kymmenen henkilöä. Tulosten perusteella muodostettiin 43 tekijän lista, jotka jaettiin kuuteen kategoriaan: sisältö-, navigointi-, visuaalisuus-, tuote-, älypuhelin- ja ympäristö ja sisäiset tekijät -kategorioihin. Merkittävät tunteet kategorioiden sisällä määritettiin tunnearviointityökalun, Geneva Emotion Wheel, avulla. Tulosten perusteella navigointitekijät ovat kaikkein merkittävimpiä shoppailukokemuksen kannalta ja tuotetekijät puolestaan shoppailun lopputuloksen kannalta. Shoppailukokemukseen ei vaikuta ainoastaan verkkokaupan design, vaan shoppailukokemuksen luonnissa on otettava huomioon ympäristö, jossa älypuhelinta selataan, kuluttajan sisäiset tekijät sekä kuluttajan älypuhelin, vaikkakin älypuhelimen merkitys tässä tutkimuksessa olikin pieni. Navigointi- ja sisältötekijät herättävät samanlaisia tunteita ja ovat tärkeitä shoppailun läpiviemisen kannalta. Visuaaliset- ja tuotetekijät puolestaan herättävät keskenään samanlaisia tunteita ja ovat tärkeitä kuluttajan kiinnostuksen herättämisessä. Tässä tutkimuksessa koottua listausta shoppailukokemukseen vaikuttavista tekijöistä ja tunteista voidaan käyttää tulevaisuudessa tutkijoiden ja yritysten toimesta tutkittaessa ja suunniteltaessa shoppailukokemusta

The Role of Social Network Websites in Consumer-Brand Relationship

This research explored the phenomenon of online social network in the context of consumer-brand relationship. The specific research objectives were: (a) to examine whether perceived benefits of a Brand’s Social Network Website (BSN) predict BSN relationship quality; (b) to investigate whether perceived benefits of BSN predict perceived relationship investment; (c) to examine if online social connection strengthens the relationship between perceived benefits of BSN and BSN relationship quality; (d) to examine if experience with BSN strengthens the relationship between perceived benefits of BSN and BSN relationship quality; (e) to investigate whether BSN relationship quality predicts brand relationship quality; (f) to examine whether BSN relationship quality predicts customer loyalty toward BSN; (g) to investigate whether perceived relationship investment predicts brand relationship quality; (h) to investigate whether brand relationship quality predicts customer loyalty toward BSN; (i) to examine whether brand relationship quality predicts customer loyalty toward the brand; and (j) to investigate whether customer loyalty toward BSN predicts customer loyalty toward the brand. This research employed a mixed-method approach to overcome the weaknesses in a single method approach and to provide stronger evidence for a conclusion. First, qualitative analyses explored the unique context of BSN, which was not much investigated in prior research. Specifically, Brand Pages of 22 apparel brands and 10 restaurant/coffeehouse brands, chosen as research settings, were investigated to validate the proposed research constructs. Second, quantitative analyses utilized an online self-administered cross-sectional survey method. A total of 501 complete responses collected from consumer panels of marketing research firm were used. The results suggested that BSN benefits are important drivers of relationship mediators (i.e., BSN relationship quality, perceived relationship investment), which in turn positively influence BRQ. However, functional benefits did not influence BSN relationship quality. In addition, while customer loyalty toward BSN was predicted by both BSN relationship quality and BRQ, it did not positively influence the loyalty toward the brand. Specifically, BSN loyalty did not influence behavioral loyalty and negatively influenced willingness to pay price premium. Further discussion about the results, implications, and suggestions for future research were provided