A framework for recommending tourist attractions using deep learning and association rule mining-based methods

Abstract. Many of tourism recommendation researches are based on the user rating and review data on the tourism platforms, and these approaches might be only suitable for a discrete recommendation for the tourist attractions. It is because each rating and review data on the platforms is created for a tourist place, not for multiple places on a travel itinerary. A travel blog data often contains information about the multiple places on a travel itinerary, but it is difficult to analyse the data compared to the rating and review data since it is like a text document having longer text than the review. In this paper, we introduce a framework consisting of a deep learning-based tourist-attraction extraction method from the blog text and an association rule mining-based recommendation method to recommend a list of tourist attractions that might be favourable to visit together in a travel itinerary

A Transfer Learning-Based Pairwise Information Extraction Framework Using BERT and Korean-Language Modification Relationships

Most named entity recognition approaches employing BERT-based transfer learning focus solely on extracting independent and simple tags, neglecting the sequence and dependency features inherent in the named-entity tags. Consequently, these basic BERT-based methods fall short in domains requiring the extraction of more intricate information, such as the detailed characteristics of products, services, and places from user reviews. In this paper, we introduce an end-to-end information extraction framework comprising three key components: (1) a tagging scheme that effectively represents detailed characteristics; (2) a BERT-based transfer learning model designed for extracting named-entity tags, utilizing both general linguistic features learned from a large corpus and the sequence and symmetric-dependency features of the named-entity tags; and (3) a pairwise information extraction algorithm that pairs features with their corresponding symmetric modifying words to extract detailed information

An Efficient MapReduce-Based Parallel Processing Framework for User-Based Collaborative Filtering

User-based collaborative filtering is one of the most-used methods for the recommender systems. However, it takes time to perform the method because it requires a full scan of the entire data to find the neighboring users of each active user, who have similar rating patterns. It also requires time-consuming computations because of the complexity of the algorithms. Furthermore, the amount of rating data in the recommender systems grows rapidly, as the number of users, items, and their rating activities tend to increase. Thus, a big data framework with parallel processing, such as Hadoop, is needed for the recommender systems. There are already many research studies on the MapReduce-based parallel processing method for collaborative filtering. However, most of the research studies have not considered the sequential-access restriction for executing MapReduce jobs and the minimization of the required full scan on the entire data on the Hadoop Distributed File System (HDFS), because HDFS sequentially access data on the disk. In this paper, we introduce an efficient MapReduce-based parallel processing framework for collaborative filtering method that requires only a one-time parallelized full scan, while adhering to the sequential access patterns on Hadoop data nodes. Our proposed framework contains a novel MapReduce framework, including a partial computation framework for calculating the predictions and finding the recommended items for an active user with such a one-way parallelized scan. Lastly, we have used the MovieLens dataset to show the validity of our proposed method, mainly in terms of the efficiency of the parallelized method

Just-in-Time Knowledge Management

Abstract. This paper presents the requirements for just in time knowledge management (JIT-KM). In order to deliver high-value information to user for decision-making, one must understand the user’s preferences, biases and decision context. A JIT-KM architecture is presented consisting of user, middleware and data services to search for information from heterogeneous sources, and to rank and deliver this to decision-makers. The search process is described using concepts from Knowledge Sifter, an agent-based system that accesses heterogeneous sources using Semantic Web Services.

Knowledge Sifter: agent-based ontology-driven search over heterogeneous databases using semantic Web services

Abstract. Knowledge Sifter is a scaleable agent-based system that supports access to heterogeneous information sources such as the Web, open-source repositories, XML-databases and the emerging Semantic Web. User query specification is supported by a user agent that accesses multiple ontologies using an integrated conceptual model expressed in the Web Ontology Language (OWL). A collection of cooperating agents supports interactive query specification and refinement, query decomposition, query processing, as well as result ranking and presentation. The Knowledge Sifter architecture is general and modular so that ontologies and information sources can be easily incorporated. A proof-of-concept implementation shows how Knowledge Sifter can search geo-spatial ontology services such as the USGS Geographic Names Information System (GNIS) and Princeton University’s WordNet as well as image databases including Lycos and TerraServer. Each Agent is implemented as a Web Service and the external sources are also accessed via Web Service Technology