Text-based Similarity Measures Research Articles

On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data

We study the spillover effects of the online reviews of other covisited products on the purchases of a focal product using clickstream data from a large retailer. The proposed spillover effects are moderated by (a) whether the related (covisited) products are complementary or substitutive, (b) the choice of media channel (mobile or personal computer (PC)) used, (c) whether the related products are from the same or a different brand, (d) consumer experience, and (e) the variance of the review ratings. To identify complementary and substitutive products, we develop supervised machine-learning models based on product characteristics, such as product category and brand, and novel text-based similarity measures. We train and validate the machine-learning models using product pair labels from Amazon Mechanical Turk. Our results show that the mean rating of substitutive (complementary) products has a negative (positive) effect on purchasing of the focal product. Interestingly, the magnitude of the spillover effects of the mean ratings of covisited (substitutive and complementary) products is significantly larger than the effects on the focal product, especially for complementary products. The spillover effect of ratings is stronger for consumers who use mobile devices versus PCs. We find the negative effect of the mean ratings of substitutive products across different brands on purchasing of a focal product to be significantly higher than within the same brand. Lastly, the effect of the mean ratings is stronger for less experienced consumers and for ratings with lower variance. We discuss implications on leveraging the spillover effect of the online product reviews of related products to encourage online purchases.

Enhanced Symbolic Description in Analyzing Patterns and Volatility on the Forex Market

In this paper, we propose a novel approach for transforming financial time-series values into symbolic representation based on value changes. Such approach seems to have few advantages over the existing approaches; one of the most obvious is noise reduction in the data and another one is possibility to find patterns which are universal for investigating different currency pairs. To achieve the goal we introduce a preprocessing method that allows initial data transformation. We also define a text-based similarity measure which can be used as an alternative for methods allowing to find the exact patterns in the historical data. To effectively evaluate our method, we present a concept that allows to predict the potential price movement direction and compare it with the actual price direction observed in the historical data. Such a method gives an opportunity not only to indicate the different price patterns based on the symbolic representation but also at the same time evaluate the predictive power of such patterns. The proposed approach is experimentally verified on 10 different currency pairs, each covering approximately a period of 10 years.

The Market Value Spillovers of Mergers and Acquisitions: Disentangling Rivalry and Technological Signaling Effects

The strategy literature emphasizes the ownership of resources or market positioning as fundamental sources of a firm's value. Building on these perspectives, this study shows that when an acquisition is announced, the market value of other non-merging companies can be simultaneously affected by revaluations of their technological capabilities and of their product market positioning. To disentangle these channels, I use text-based similarity measures to define technological and product proximity to the acquired company. Technological revaluation is driven by technology similarities and is moderated by factors signaling undervaluation. Product market revaluation is driven by product proximity and is moderated by the expected competitive effects of the merger. The more dynamic the product market environment, the more technological revaluation effects dominate product market revaluations.

SimCC: A novel method to consider both content and citations for computing similarity of scientific papers

To compute the similarity of scientific papers, text-based similarity measures, link-based similarity measures, and hybrid methods can be applied. The text-based and link-based similarity measures take into account only a single aspect of scientific papers, content or citations, respectively. The hybrid methods consider both content and citations; however, they do not carefully consider the relation between the content of a pair of papers involved in a citation relationship. In this paper, we propose a novel method, SimCC (similarity based on content and citations), that considers both aspects, content and citations, to compute the similarity of scientific papers. Unlike previous methods, SimCC effectively reflects both content and authority of scientific papers simultaneously in similarity computation by applying a new RA (relevance and authority) weighting scheme. Also, we propose an RA+R weighting scheme to consider the recency of papers and an RA+E weighting scheme to take into account the author expertise of papers in similarity computation. The effectiveness of our proposed method is demonstrated by extensive experiments on a real-world dataset of scientific papers. The results show that our method achieves more than 100% improvement in accuracy in comparison with previous methods.

Significance Estimation For Sequence-Based Chemical Similarity Searching (PhAST) And Application To AuroraA Kinase Inhibitors

Chemical similarity searching allows the retrieval of preferred screening molecules from a compound database. Candidates are ranked according to their similarity to a reference compound (query). Assessing the statistical significance of chemical similarity scores helps prioritizing significant hits, and identifying cases where the database does not contain any promising compounds. Our text-based similarity measure, Pharmacophore Alignment Search Tool (PhAST), employs pair-wise sequence alignment. We adapted the concept of E-values as significance estimates and employed a sampling technique that incorporates the principle of importance sampling in a Markov chain Monte Carlo simulation to generate distributions of random alignment scores. These distributions were used to compute significance estimates for similarity scores in a preliminary prospective virtual screen for inhibitors of Aurora A kinase. Assessing the significance of compound similarity computed with PhAST allows for a statistically motivated identification of candidate screening compounds. Inhibitors of Aurora A kinase were retrieved from a large compound library.