Recommender System Based on Consumer Product Reviews

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

Informed recommender: basing recommendations on consumer product reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology

Informed recommender: basing recommendations on consumer product reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology

Recommender System Based on Consumer Product Reviews

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

Assimilation and contrast in web product reviews: devaluing the recommendation of a proficient but dissimilar reviewer

When viewing web-consumer reviews consumers encounter the reviewers in an anonymous environment. Although their interactions are only virtual they still exchange social information, e.g. often reviewers refer to their proficiency or consumption motives within the review texts. Do these social information harm the viewers’ perception of the recommended products? The present study addresses this question by applying the paradigm of social comparison (Mussweiler, 2003) to web-consumer reviews. In a laboratory experiment with a student sample (n = 120) we manipulated the perceived similarity between reviewer and viewer and the perceived proficiency of the reviewer. A measurement of achievement goals (Elliott & McGregor, 2001) and average number of hours of study prior to the experiment allowed to introduce the reviewer as high [low] in proficiency and similar [dissimilar] in achievement goals. As predicted, the viewer’s evaluation of the recommended products differed as a function of this social information. Contrasting with the reviewer led to devaluing the products recommended by a proficient but dissimilar reviewer. However, against our prediction social comparison with the reviewer did not affect the viewer`s self-evaluation. Whether social information in web-product reviews affects the viewer`s self-evaluation and induces both social comparison processes remains an open question. Future studies aim to address this by manipulating the informational focus of the viewer, rather than the perceived similarity between viewer and reviewer. So far, the present study extends the application of social comparison to consumption environments and contributes to the understanding of the virtual social identity.

Leveraging product adopter information from online reviews for product recommendation

The availability of the sheer volume of online product reviews makes it possible to derive implicit demographic information of product adopters from review documents. This paper proposes a novel approach to the extraction of product adopter mentions from online reviews. The extracted product adopters are the ncategorise into a number of different demographic user groups. The aggregated demographic information of many product adopters can be used to characterize both products and users, which can be incorporated into a recommendation method using weighted regularised matrix factorisation. Our experimental results on over 15 million reviews crawled from JINGDONG, the largest B2C e-commerce website in China, show the feasibility and effectiveness of our proposed frame work for product recommendation.

We know what you want to buy:a demographic-based system for product recommendation on microblogs

Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time. © 2014 ACM.

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.

E-Natural

[ES] E-NATURAL es un portal web donde se localizan empresas del sector del turismo rural, en este portal se publicitan y venden sus productos y servicios. Cada empresa dispone de un espacio web único e individual para poder promocionarse en internet. Mediante un buscador, permite a los usuarios acceder a los contenidos de cada empresa registrada en el sistema. Este buscador es abierto y cualquier usuario no registrado puede consultar la información acerca de productos y servicios ofertados, y disponer de toda la información relacionada con cada empresa. Las empresas registradas disponen de un sistema de información completo de fácil manejo e intuitivo que permite autogestionar todo el contenido de los productos y páginas web de cada empresa individualmente. También se incluye  un sistema de gestión de contenidos que genera páginas web profesionales automáticamente, con posibilidad de edición de páginas. Por otra parte, los usuarios registrados podrán realizar: reservas de productos mediante  un completo sistema de gestión de reservas, con especial atención al alojamiento, compras de productos mediante un completo sistema de compras, adaptado a la plataforma Paypal, clasificaciones de productos y páginas web del sistema, utilizando votaciones mediante rankings. La plataforma contiene un sistema de gestión de comentarios sobre productos y páginas web de empresas que permite seleccionar la visualización y la no visualización del contenido. Por último, los usuarios podrán compartir información sobre contenidos publicados en las páginas, mediante el uso de redes sociales como Twitter, Google+ y Facebook.

Incorporating sentiment prior knowledge for weakly supervised sentiment analysis

This article presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word distribution (LDA-DP), the other is by augmenting the model objective function through adding terms that express preferences on expectations of sentiment labels of the lexicon words using generalized expectation criteria (LDA-GE). We conducted extensive experiments on English movie review data and multi-domain sentiment dataset as well as Chinese product reviews about mobile phones, digital cameras, MP3 players, and monitors. The results show that while both LDA-DP and LDAGE perform comparably to existing weakly supervised sentiment classification algorithms, they are much simpler and computationally efficient, rendering themmore suitable for online and real-time sentiment classification on the Web. We observed that LDA-GE is more effective than LDA-DP, suggesting that it should be preferred when considering employing the topic model for sentiment analysis. Moreover, both models are able to extract highly domain-salient polarity words from text.

Mining product adopter information from online reviews for improving product recommendation

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization formore effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graphbased method to iteratively update user- and product-related distributions more reliably in a heterogeneous user-product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JINGDONG, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.

New Product Development (NPD) Success Factors: A Review of the Literature

This paper reviews the literature on managerially actionable new product development success factors and summarises the field in a classic managerial framework. Because of the varying quality, breadth and scope of the field, the review only contains post-1980 studies of tangible product development that are of a rigorous scientific standard. Success is interpreted as a commercial success. The field has gained insight into a broad set of factors that vary in scope, abstraction and context. Main areas that contribute to NPD success are top management support exhibited through resource allocation and communicating the strategic importance of NPD in the organisation. The right projects need to be selected for investment at the beginning of the process and should be aligned to the organisation's internal competencies and the external environment. The NPD process should use cross-functional teams and a competent project champions. Marketing research competency is crucial, as an understanding of the market, customers and competitors is repeatedly highlighted. Product launch competency was also consistently shown to be important. In terms of controlling the NPD process, strict project gates are required to maintain control.

Outsourcing process in pharmaceutical product development

The aim of this study was to create an outsourcing process for pharmaceutical product development. This study focuses on two main questions. The first question is “What is the outsourcing process model?” In the second phase key success factors of the outsourcing process are identified. As a result of the literature reviews, a general outsourcing process was created. Transaction cost economics and resource based view were used to derived a theoretical framework to the process by combining the existing processes presented in the literature. The model of process is considered used to the outsourcing broadly. The general outsourcing process was then developed further with the key factors that affect the success of pharmaceutical product development and the interviews of pharmaceutical outsourcing experts. The result of the research was the process consists of seven phases with key activities and expected outputs for each of the phases. In addition, the strategic decision-making framework for outsourcing decision in pharmaceutical product development is giving as well as the tools for selecting supplier and preparing structured contract. This study also gives some recommendations for managing the outsourcing process.