Improving diffusion-based recommendation in online rating systems

Network diffusion processes play an important role in solving the information overload problem. It has been shown that the diffusion-based recommendation methods have the advantage to generate both accurate and diverse recommendation items for online users. Despite that, numerous existing works consider the rating information as link weight or threshold to retain the useful links, few studies use the rating information to evaluate the recommendation results. In this paper, we measure the average rating of the recommended products, finding that diffusion-based recommendation methods have the risk of recommending low-rated products to users. In addition, we use the rating information to improve the network-based recommendation algorithms. The idea is to aggregate the diffusion results on multiple user-item bipartite networks each of which contains only links of certain ratings. By tuning the parameters, we find that the new method can sacrifice slightly the recommendation accuracy for improving the average rating of the recommended products.

Comparing the Impact of Online Ratings and Report Cards on Patient Choice of Cardiac Surgeon: Large Observational Study (Preprint)

BACKGROUND Patients may use two information sources about a health care provider’s quality: online physician reviews, which are written by patients to reflect their subjective experience, and report cards, which are based on objective health outcomes. OBJECTIVE The aim of this study was to examine the impact of online ratings on patient choice of cardiac surgeon compared to that of report cards. METHODS We obtained ratings from a leading physician review platform, Vitals; report card scores from Pennsylvania Cardiac Surgery Reports; and information about patients’ choices of surgeons from inpatient records on coronary artery bypass graft (CABG) surgeries done in Pennsylvania from 2008 to 2017. We scraped all reviews posted on Vitals for surgeons who performed CABG surgeries in Pennsylvania during our study period. We linked the average overall rating and the most recent report card score at the time of a patient’s surgery to the patient’s record based on the surgeon’s name, focusing on fee-for-service patients to avoid impacts of insurance networks on patient choices. We used random coefficient logit models with surgeon fixed effects to examine the impact of receiving a high online rating and a high report card score on patient choice of surgeon for CABG surgeries. RESULTS We found that a high online rating had positive and significant effects on patient utility, with limited variation in preferences across individuals, while the impact of a high report card score on patient choice was trivial and insignificant. About 70.13% of patients considered no information on Vitals better than a low rating; the corresponding figure was 26.66% for report card scores. The findings were robust to alternative choice set definitions and were not explained by surgeon attrition, referral effect, or admission status. Our results also show that the interaction effect of rating information and a time trend was positive and significant for online ratings, but small and insignificant for report cards. CONCLUSIONS A patient’s choice of surgeon is affected by both types of rating information; however, over the past decade, online ratings have become more influential, while the effect of report cards has remained trivial. Our findings call for information provision strategies that incorporate the advantages of both online ratings and report cards.

Two Models Based on Social Relations and SVD++ Method for Recommendation System

Recently, Recommender Systems (RSs) have attracted many researchers whose goal is to improve the performance of the prediction accuracy of recommendation systems by alleviating RSs drawbacks. The most common limitations are sparsity and the cold-start problem. This article proposes two models to mitigate the effects of these limitations. The proposed models exploit five sources of information: rating information, which involves two sources, namely explicit and implicit, which can be extracted via users’ ratings, and two types of social relations: explicit and implicit relations, the last source is confidence values that are included in the first model only. The whole sources are combined into the Singular Value Decomposition plus (SVD++) method. First, to extract implicit relations, each non-friend pair of users, the Multi-Steps Resource Allocation (MSRA) method is adopted to compute the probability of being friends. If the probability has accepted value which exceeds a threshold, an implicit relationship will be created. Second, the similarity of explicit and implicit social relationships for each pair of users is computed. Regarding the first model, a confidence value between each pair of users is computed by dividing the number of common items by the total number of items which have also rated by the first user of this pair. The confidence values are combined with the similarity values to produce the weight factor. Furthermore, the weight factor, explicit, and implicit feedback information are integrated into the SVD++ method to compute the missing prediction values. Additionally, three standard datasets are utilized in this study, namely Last.Fm, Ciao, and FilmTrust, to evaluate our models. The experimental results have revealed that the proposed models outperformed state-of-the-art approaches in terms of accuracy.

Enhanced hotel recommendation method addressing the deviation between overall rating and detailed criteria ratings on Tripadvisor.com

User rating information on multiple predefined aspects gathered by hotel recommendation systems generally shows a deviation between the overall rating and detailed criteria ratings. In this study, to address this deviation, we proposed a novel hotel recommendation method that clusters users with different preferences into different groups using the K-means algorithm. Moreover, we allocated weights to different criteria and obtained a comprehensive score. A case study on actual data from Tripadvisor.com showed that compared with three other models, our proposed model demonstrated a more impressive performance. This research can offer advantages to hotel service providers and customers in terms of decision making.

Evaluating Rank-Coherence of Crowd Rating in Customer Satisfaction

Crowd rating is a continuous and public process of data gathering that allows the display of general quantitative opinions on a topic from online anonymous networks as they are crowds. Online platforms leveraged these technologies to improve predictive tasks in marketing. However, we argue for a different employment of crowd rating as a tool of public utility to support social contexts suffering to adverse selection, like tourism. This aim needs to deal with issues in both method of measurement and analysis of data, and with common biases associated to public disclosure of rating information. We propose an evaluative method to investigate fairness of common measures of rating procedures with the peculiar perspective of assessing linearity of the ranked outcomes. This is tested on a longitudinal observational case of 7 years of customer satisfaction ratings, for a total amount of 26.888 reviews. According to the results obtained from the sampled dataset, analysed with the proposed evaluative method, there is a trade-off between loss of (potentially) biased information on ratings and fairness of the resulting rankings. However, computing an ad hoc unbiased ranking case, the ranking outcome through the time-weighted measure is not significantly different from the ad hoc unbiased case.

Determinan Peringkat Sukuk: Ditinjau dari Aspek Akuntansi dan Non-Akuntansi

The objective of this study was to provide empirical findings whether firm size, liquidity, productivity, and sukuk maturity have a significant influence on sukuk ratings. This study uses secondary data. The population of this research is companies issuing sukuk, listed on The Indonesia Stock Exchange and rated by PT PEFINDO. The sampling method used was purposive sampling. Meanwhile, for data analysis this study uses panel data regression method with the assistance of Eviews 10 software. Based on analysis, the results of this study indicate that company size, liquidity, profitability, and productivity have a positive effect on the sukuk rating, while sukuk maturity has a negative effect on the sukuk rating. Several implications of the results of this study. First, sukuk investors will be helped by sukuk rating information, because it reflects the company's financial condition. Second, companies that will issue sukuk or existing sukuk issuers must always put concern on the financial condition of their companies to ensure that the sukuk issued have a good rating. Third, the sukuk issuer must have careful planning to determine sukuk maturity, because it is associated with risks. Fourth, to reduce information asymmetry, policymakers should require all sukuk issuers to rate their sukuk. Keywords: Sukuk Ratings; Company Size; Liquidity; Profitability; Productivity; and Sukuk Maturity

A Hybrid Portfolio Selection Model

It is generally observed that investors approach asset managers and financial analysts to recommend a customized portfolio based on certain personalized preferences. The article discusses a methodology to build a hybrid personalized multi-criteria model in the Indian stock market context suiting investor preferences. The analytical hierarchy process (AHP) was used to compute the criteria weights and data envelopment analysis (DEA) was adopted to screen the best portfolios which were subsequently ranked by a fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) and evaluation based on distance from average solution (EDA). The rankings of portfolios were validated for robustness with the actual rankings awarded by Credit Rating Information Services of India Limited (CRISIL) to demonstrate the efficacy of the hybrid model and it was found that Fuzzy TOPSIS and EDA rankings were consistent with the CRISIL rankings proposed by expert investors.