Text-mining technologies have substantially affected financial industries. As the data in every sector of finance have grown immensely, text mining has emerged as an important field of research in the domain of finance. Therefore, reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research. This paper focuses on the text-mining literature related to financial forecasting, banking, and corporate finance. It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies. Finally, the paper briefly discusses various text-mining methods being applied in the financial domain, the challenges faced in these applications, and the future scope of text mining in finance.

/pdf/comprehensive-review-of-text-mining-applications-in-finance-2t5w0suhu1.pdf

Comprehensive review of text-mining applications in finance

Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available dataset containing user feedback regarding serendipity. We compared our SOG algorithm with topic diversification, popularity baseline, singular value decomposition, serendipitous personalized ranking and Zheng’s algorithms relying on the above dataset. SOG outperforms other algorithms in terms of serendipity and diversity. It also outperforms serendipity-oriented algorithms in terms of accuracy, but underperforms accuracy-oriented algorithms in terms of accuracy. We found that the increase of diversity can hurt accuracy and harm or improve serendipity depending on the size of diversity increase.

/pdf/how-does-serendipity-affect-diversity-in-recommender-systems-2pvk69s4v3.pdf

How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm

A recommender system is employed to accurately recommend items, which are expected to attract the user’s attention. The over-emphasis on the accuracy of the recommendations can cause information over-specialization and make recommendations boring and even predictable. Novelty and diversity are two partly useful solutions to these problems. However, novel and diverse recommendations cannot merely ensure that users are attracted since such recommendations may not be relevant to the user’s interests. Hence, it is necessary to consider other criteria, such as unexpectedness and relevance. Serendipity is a criterion for making appealing and useful recommendations. The usefulness of serendipitous recommendations is the main superiority of this criterion over novelty and diversity. The bulk of studies of recommender systems have focused on serendipity in recent years. Thus, a systematic literature review is conducted in this paper on previous studies of serendipity-oriented recommender systems. Accordingly, this paper focuses on the contextual convergence of serendipity definitions, datasets, serendipitous recommendation methods, and their evaluation techniques. Finally, the trends and existing potentials of the serendipity-oriented recommender systems are discussed for future studies. The results of the systematic literature review present that the quality and the quantity of articles in the serendipity-oriented recommender systems are progressing.

Serendipity in Recommender Systems: A Systematic Literature Review

During last twenty years recommender system have emerged as a research field. Recommender System is rooted in the field of Information Retrieval, Machine Learning and Decision Support System. Most of the users do not have enough knowledge to make automatic decisions. So they need recommendation of different items for better choice. Because of this many researchers tried to understand the algorithmic techniques for recommendation to the given user. It is very important factor to identify similar items related to the target user’s test. To find similar items RS uses item preference of an item. In different RSs, the item preferences are available in different forms, i.e. preferences are either available, Boolean preference (yes/no) or not available. We test various User Similarity Measures for dataset with preferences, without preferences and Boolean preferences. We tested various similarity measures for User Based Collaborative Filtering techniques in Apache Mahout.

Use of Similarity Measure in Recommender System Based on Type of Item Preferences

Collaborative filtering (CF) technique in recommender systems (RS) is a well-known and popular technique that exploits relationships between users or items to make product recommendations to an active user. The effectiveness of existing memory based algorithms depend on the similarity measure that is used to identify nearest neighbours. However, similarity measures utilize only the ratings of co-rated items while computing the similarity between a pair of users or items. In most of the e-commerce applications, the rating matrix is too sparse since even active users of an online system tend to rate only a few items of the entire set of items. Therefore, co-rated items among users are even sparser. Moreover, the ratings a user gives an individual item tells us nothing about his comprehensive interest without which the generated recommendations may not be satisfactory to a user. In order to be able to address these issues, a comprehensive study is made of the various existing measures of similarity in a collaborative filtering recommender system (CFRS) and a hierarchical categorization of products has been proposed to understand the interest of a user in a wider scope so as to provide better recommendations as well as to alleviate data sparsity.

Measures of similarity in Memory-Based collaborative filtering recommender system

/pdf/serendipity-in-recommender-systems-ejs9ybhbvu.pdf

Serendipity in Recommender Systems

Nowadays, Recommender System is quite a useful system which helps people to navigate through complex items. There are many different recommender systems proposed and many business solutions have been found. But, because of the high complexity and uncertainty of the problem there is no best approach found. Recent research in the stream not just limited to the accuracy of the system. There are many another factors such as serendipity means as surprise, is defined as the finding the unexpected as well as useful items for the user. In our system, we are providing serendipity by the intrinsic accidents and user will find the value by applying their knowledge on it which says sagacity. We considered this mechanism as serendipity-oriented recommender system. Our idea is to give serendipity-oriented recommender system by Item Amalgamation Approach. The core logic of this technique is to find the items which have common features of the given input items by the user. We are considering benchmark and well known dataset MovieLens dataset for our Approach.

Item Amalgamation Approach for Serendipity-Oriented Recommender System

The stock market prediction has always been a subject of interest since the emergence of prediction techniques. Because of the abundance of data available on the Internet in past years, it has been getting easier for all types of prediction. Various prediction and analysis models have been developed, majorly using the concepts of Machine Learning and Data Mining. Each model has its own advantage as well as limitations. Also, stock prices tend to fluctuate because of many factors like a national policy, or industry related political news. Many advanced analysis and prediction models do take these factors into consideration while some elementary models do not. In this paper, the analysis and its subsequent evaluation of stock prices are done using real-time data; based on the concepts of Support Vector Regression. Further, in this paper, the predicted stock prices are compared with the actual stock prices to have an idea regarding the accuracy of this technique.

/pdf/comparison-of-differentkernels-of-support-vector-machine-for-2mp2wcllwp.pdf

COMPARISON of DIFFERENTKERNELS of SUPPORT VECTOR MACHINE for PREDICTING STOCK PRICES

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Serendipity in Recommender Systems

Item Amalgamation Approach for Serendipity-Oriented Recommender System

Use of Similarity Measure in Recommender System Based on Type of Item Preferences

COMPARISON of DIFFERENTKERNELS of SUPPORT VECTOR MACHINE for PREDICTING STOCK PRICES