This paper reveals the characteristics of the reviews that encourage readers to watch the reviewed movie by analyzing large-scale access log data. We assume that some of the reviews that users saw just before they clicked the links to a streaming site contain factors that help users decide whether they watch that movie. Our method used a random forest classifier trained to determine whether a review encouraged a movie-watching behavior. We conducted feature importance-based analysis using three types of features: review itself, item, and reviewer. We analyzed 70,000 user behaviors from Yahoo! Movies (a movie review site in Japan) and Gyao! (a movie streaming site in Japan). Through a cross-validation experiment, the classifier was able to classify encouraging reviews with an F-score of 0.78, and mainly the features about the item contributed to the classification performance. An additional subjects experiment confirmed that these features contribute to the review’s usefulness.

This paper proposes a method for extracting diverse expressions from online movie review texts for a given keyword query. When people watch a movie that makes them cry, they generally do not say “I cried.” Instead, they use such euphemistic language as “I needed a handkerchief” or “My makeup was running.” To enable information retrieval based on audience reactions such as “movies that make me cry” using review texts, a variety of paraphrased expressions must be collected for arbitrary queries. Our proposed method extracts such expressions from review datasets by applying two extensions to Doc2Vec: 1) it changes the granularity of the training sentences to mitigate a lack of context, and 2) it applies query expansion for similarity calculation in advance. We conducted a large-scale experiment using crowdsourcing with 1.29 million actual sentences taken from Yahoo! Movies, Japan. The experimental result revealed that changing the training data granularity and adding the query expansion are both effective to accurately collect more diverse expressions that have a meaning similar to the given query.

This paper proposes a method for ranking movies by keyword queries and examples using machine learning techniques that analyze actual data from the online movie review site. Existing search methods cannot rank movies in “surprising order” for the keyword query “surprising.” People and critics created many “My best surprising movies” rankings on the web. Our proposed method uses a LambdaMART, one of the mainstream Learning to Rank techniques, to learn these personalized rankings and sort the movies through the viewpoint represented by a given query. To accept more complex information needs, we diverted the learning results to a search-by-example algorithm that enables users to input examples, such as “surprising movies like The Usual Suspects or Fight Club.” The experiment using the personal ranking data from the personal content curation service in Yahoo! Movies Japan suggests two findings: direct learning of personal ranking does not improve search performance, and the search-by-example-based application increases user satisfaction.

This paper proposes a method of ranking geographic entities (places) where the purpose given as a query can be achieved. Most existing map search engines accept only the name of a place or the type of a place. Thus when searchers want to find a suitable place for “guitar practice”, they have to input a place type such as “music studio”. To create such a query, prior knowledge (i.e., that a music studio is suitable for playing guitar) is required. Our proposed method uses online review information on places to enable direct place retrieval from a given purpose query. Our method creates a bipartite graph consisting of places and the words that appear in the reviews of these places. The relevance between the given keyword query and a place is calculated by using the Random Walk with Restart algorithm. Additionally, we expand the graph with three hypotheses; 1) places that are suitable for the same purpose are similar to each other, and purposes that can be achieved in the same place are similar to each other, 2) the same purpose can be achieved in places with similar metadata, and 3) purposes which have semantically similar meaning can be achieved in the same places. Through an experiment using real review data taken from Google Maps, the usefulness of the proposed method was demonstrated. In particular, it was found that the expansion by places’ metadata is effective for finding more relevant places.

This paper proposes a new method of supplementing the context of short sentences for the training phase of Doc2Vec. Since CGM (Consumer Generated Media) sites and SNS sites become widespread, the importance of similarity calculation between a given query and a short sentence is increasing. As an example, a search by the query "sad" should find actual expressions such as "I needed a handkerchief" on a movie review site. Doc2Vec is one of the most widely used methods for vectorization of queries and sentences. However, Doc2Vec often exhibits low accuracy if the training data consists of short sentences, because they lack context. We modified Doc2Vec with the hypothesis that other posts for the same topic (i.e. reviews for the same movie in online movie review sites) may share the same background. Our method uses target-topic IDs instead of sentence IDs as the context in the training phase of the Doc2Vec with the PV-DM model; this model estimates the next term from a few previous terms and context. The model trained with item IDs vectorizes a sentence more accurately than a model trained with sentence IDs. We conducted a large-scale experiment using 1.2 million movie review posts and a crowdsourcing-based evaluation. The experimental result demonstrates that our new method achieves higher precision and nDCG than previous Doc2Vec variants and traditional topic modeling methods.