Analysis of Yelp Reviews

In the era of Big Data and Social Computing, the role of customer reviews and ratings can be instrumental in predicting the success and sustainability of businesses. In this paper, we show that, despite the apparent subjectivity of user ratings, there are also external, or objective factors which help to determine the outcome of a business's reviews. The current model for social business review sites, such as Yelp, allows data (reviews, ratings) to be compiled concurrently, which introduces a bias to participants (Yelp Users). Our work examines Yelp Reviews for businesses in and around college towns. We demonstrate that an Observer Effect causes data to behave cyclically: rising and falling as momentum (quantified in user ratings) shifts for businesses.Comment: 24 pages, 20 figures and 5 table

A random walk method for alleviating the sparsity problem in collaborative filtering

Collaborative Filtering is one of the most widely used ap-proaches in recommendation systems which predicts user preferences by learning past user-item relationships. In re-cent years, item-oriented collaborative filtering methods came into prominence as they are more scalable compared to user-oriented methods. Item-oriented methods discover item-item relationships from the training data and use these re-lations to compute predictions. In this paper, we propose a novel item-oriented algorithm, RandomWalk Recommender, that first infers transition probabilities between items based on their similarities and models finite length random walks on the item space to compute predictions. This method is especially useful when training data is less than plentiful, namely when typical similarity measures fail to capture ac-tual relationships between items. Aside from the proposed prediction algorithm, the final transition probability matrix computed in one of the intermediate steps can be used as an item similarity matrix in typical item-oriented approaches. Thus, this paper suggests a method to enhance similarity matrices under sparse data as well. Experiments on Movie-Lens data show that RandomWalk Recommender algorithm outperforms two other item-oriented methods in different sparsity levels while having the best performance difference in sparse datasets

Observation of implicit complexity by non confluence

We propose to consider non confluence with respect to implicit complexity. We come back to some well known classes of first-order functional program, for which we have a characterization of their intentional properties, namely the class of cons-free programs, the class of programs with an interpretation, and the class of programs with a quasi-interpretation together with a termination proof by the product path ordering. They all correspond to PTIME. We prove that adding non confluence to the rules leads to respectively PTIME, NPTIME and PSPACE. Our thesis is that the separation of the classes is actually a witness of the intentional properties of the initial classes of programs