Consumer reviews have emerged as one of the most influential factors in a person's purchase behavior. The existing open-source approaches for detecting expert reviewers and determining product ratings suffer from limitations and are susceptible to manipulation. In this work, we addressed these limitations by developing two algorithms and evaluated them on three datasets from amazon.com (the largest dataset contains nearly eight million reviews). In the first algorithm, we used a combination of the existing open-source approaches such as filtering by volume of contribution, helpfulness ratio, volume of helpfulness, and deviation from the estimated actual rating to detect the experts. The second algorithm is based on link analytic mutual iterative reinforcement of product ratings and reviewers' weights. In the second algorithm, both reviewers and products carry weights reflecting their relative importance. The reviewers influence the product rating according to their weight. Similarly, the reviewers' weights are impacted by their amount of deviation from the estimated actual product rating and the product's weight. Our evaluation using three datasets from amazon.com found the second algorithm superior to the other algorithms in detecting experts and deriving product ratings, significantly reducing the avg. error and avg. Mean Squared Error of the experts over the best of the other algorithms even after maintaining similar product coverage and quantity of reviews.

No abstract

No abstract

In this paper we report the splicing losses of a single mode optical fiber at different humid conditions and at different places of Bangladesh Railway West Zone (BRWZ). The location of the splicing points and their losses for a single mode step index optical fiber have been measured by optical time domain reflectometer before re-splicing. Fusion splicer is used to re-splice these points at different humid conditions to analyze the splicing loss dependency. From this study it has been found that different humid conditions affect the splicing losses to a large extent.

No abstract