Semantic Term-Term Coupling based Feature Enhancement of User Profiles in Recommendation Systems

Content-based recommender system is a subclass of information systems that recommends an item to the user based on its description. It suggests items such as news, documents, articles, webpages, journals, and more to users as per their inclination by comparing the key features of the items with key terms or features of user interest profiles. This paper proposes the new methodology using Non-IIDness based semantic term-term coupling from the content referred by users to enhance recommendation results. In the proposed methodology, the semantic relationship is analyzed by estimating the explicit and implicit relationship between terms. It associates terms that are semantically related in real world or are used inter-changeably such as synonyms. The underestimated features of user profiles have been enhanced after term-term relation analysis which results in improved similarity estimation of relevant items with the user profiles.The experimentation result proves that the proposed methodology improves the overall search and retrieval results as compared to the state-of-art algorithms.

Z problematyki opisu frazeologii publicystycznej

The article analyses the sources of phraseological units occurring in the media and in journalistic texts of contemporary Polish language. The first part of the article contains a few remarks on two types of new noun phrases, e.g. Europa dwu prędkości (two-/multi-speed Europe), mowa nienawiści (hate speech), dane wrażliwe (sensitive data). The second part indicates the main problems related to the linguistic description of phraseology in journalism: the phraseologism – term relation and the difficulties in identifying the sources of phraseological units (the type of loanword).

Tourism and Counterurbanization in a Low-Amenity Peripheral Island: A Longitudinal Study at Yakushima Island in Kagoshima, Japan

This research explored the long-term relation between tourism development and counterurbanization in a remote island in Japan, as the longevity of in-migrants’ role in low-amenity tourism destinations has been questioned. Using data collected over 10 years at Yakushima Island, the study investigated the island’s population trend, in-migrants’ motivation for relocation, their contributions to tourism, and the lives on the island. The results showed that the trend of population growth differed among Yakushima’s 24 villages likely because of accessibility, proximity to tourism attractions, the weather, and housing availability. Yakushima’s natural environment was the key factor in in-migrants’ migration choice. Encounters and connections with people on the island were found to be another important factor. In-migrants introduced ecotours as an innovation in the 1990s, and thereafter, many in-migrants moved to Yakushima with high aspirations of becoming tour guides. Tourism stagnated starting in 2008, and some in-migrants began moving out of the island. Despite the overall downward trend of tourism, an increase in international tourists created a niche market before the COVID-19 pandemic, attracting foreign in-migrants as tourism entrepreneurs in recent years. Similar to the main driver for Japanese in-migrants’ relocation, nature was also the main motivation for international tourists’ relocation.

Carbon dioxide atmospheric concentration and hydrometeorological disasters

We study the long-run dynamic and predictive connection between atmospheric carbon dioxide (CO2) concentration and the probability of hydrometeorological disasters. For a panel of 193 countries over the period 1970-2016 we estimate the probabilities of hydrometeorological disasters at country levels by means of Bayesian sampling techniques. We then separate the effects of climatological and socio-demographic factors (used as proxies for exposure and vulnerability) and other country-specific factors, from a global probability of disasters (GPOD). Finally, we subject these global probability time paths to a cointegration analysis with CO2 concentration and run projections to year 2040 of the GPOD conditional on nine Shared Socioeconomic Pathways scenarios. We detect a stable long-term relation between CO2 accumulation and the GPOD that allows to determine projections of the latter process conditional on the former. This way, we demonstrate that generally and readily available statistical data on CO2 global atmospheric concentrations can be used as a conceptually meaningful, statistically valid and policy useful predictor of the probability of occurrence of (global) hydrometeorological disasters.

Dynamic impact of Gold Prices, Oil Prices and Exchange Rate on Stock Market Performance: A Case of Pakistan’s Stock Exchange (KSE 100 Index)

Currently emerging markets are passing through economic turmoil due to considerable increases in the prices of oil and gold with significant variation in the foreign exchange market. All the macroeconomic variables are touching the highest value which was never occurred in the history of Pakistan. Taking advantages of the current situation the study has examined the impact of gold prices, oil prices and exchange rate on stock market performance. For this purpose, the study has used daily data of these macroeconomic variables for the period of 2003 to 2018. By using time series data analysis, it reveals that there is no co-integration or long-term relation among these variables; however, the vector autoregressive model showed significant short-term relation among the securities market performance, foreign exchange rate, prices of oil and gold. The analysis also suggests that significant changes in the prices of oil, foreign exchange rates and the prices of gold have a negative lagged effect on the performance of the stock market.