Youtube is one of the most popular video sharing online resource that has millions of users around the world. The huge bulk of videos, which are growing at a high rate is posing problems for users to traverse through to relevant content. Users are facilitated with recommended videos that appeal to there interests. Following a hybrid recommendation approach, videos are recommended based on both collaborative recommendation and content-based recommendation. A limitation associated to this approach is that the videos recommended may not necessarily be appropriate to the current context that the user is in. Its very common for a single user to follow different interests depending of on the context they are in. A context-aware recommender system is proposed for Youtube that keeps track of multiple interests of a user and recommends videos based on their current context only. It serves a user better in finding relevant videos and has higher relevance to human judgment.

Context-aware video recommender systems (CAVRS) seek to improve recommendation performance by incorporating contextual features along with the conventional user-item ratings used by video recommender systems. In addition, the selection of influential and relevant contexts has a significant effect on the performance of CAVRS. However, it is not guaranteed that, under the same contextual scenario, all the items are evaluated by users for providing dense contextual ratings. This problem cause contextual sparsity in CAVRS because the influence of each contextual factor in traditional CAVRS assumes the weights of contexts homogeneously for each of the recommendations. Hence, the selection of influencing contexts with minimal conflicts is identified as a potential research challenge. This study aims at resolving the contextual sparsity problem to leverage user interactions at varying contexts with an item in CAVRS. This problem may be investigated by considering a formal approximation of contextual attributes. For the purpose of improving the accuracy of recommendation process, we have proposed a novel contextual information selection process using Soft-Rough Sets. The proposed model will select a minimal set of influencing contexts using a weights assign process by Soft-Rough sets. Moreover, the proposed algorithm has been extensively evaluated using “LDOS-CoMoDa” dataset, and the outcome signifies the accuracy of our approach in handling contextual sparsity by exploiting relevant contextual factors. The proposed model outperforms existing solutions by identifying relevant contexts efficiently based on certainty, strength, and relevancy for effective recommendations.

The adoption of the contextual information in recommender systems is fairly recent. Context-Aware Recommender Systems (CARS) provide better-personalized recommendations by utilizing contextual features in comparison to the classical two-dimensional recommendation process. In addition, selection of the specific recommender algorithm has a direct impact on the performance of CARS. In the context of video recommender systems, roughness exist in deciding which context is better to choose when a user had watched same video in more than one contextual scenarios. Therefore, selection of appropriate context and recommender algorithm for developing CARS is identified as potential research challenge. This problem may be investigated by considering a formal approximation on contextual attributes. In this article, we investigate the applicability of using soft-rough sets for formulating such an approximation on a sample real world scenario. The main objective of this study is demonstrating the applicability of soft-rough sets on CARS for removing roughness and improving contextual information selection process. The experimental results revealed that only 27% of the rules were identified by applying Rough sets on context-aware video recommender systems (CAVRS). These results can be further improved using Soft-rough sets by representing the given scenario in Boolean-valued information systems. Additionally, a few recommendations for future work on CARS are proposed.

Software requirement engineering is among the most important issues for starting any software project. The most-reported problem in requirement engineering (RE) is the difficulty to identify quality requirements. Sometime analysts may face incorrect and incomplete requirements, which may become the reason for project failures from the perspective of dissatisfaction of stakeholders. Hence, the quality in each phase of RE is important. The main focus of this article is to categorizing, Identifying, and synthesizing the existing researches on quality enhancement of the RE process. The systematically reviewing the relevant studies based on the Kitchenham systematic review methodology is the main objective of this study. This review identifies the methods which are used for enhancing the quality of RE by improving requirement elicitation, requirement analysis and specification, and requirement validation process. In this research, we are not specific to any phase of RE. Hence, the techniques obtained for quality enhancement purpose can target different phases as we are going to deal with the overall quality of requirements to enhance different quality factors as defined by IEEE standards. In general, 3 defined research questions have been invested for the sake of discussion on explored results. Likewise, 44 articles were selected from an initial set of 101 research papers. In particular, research articles answering formulated question have been included in this SLR.

Context-aware recommender system (CARS) plays a vital role to paved way for improving traditional recommendation phenomena. A key issue in CARS is the selection of influencing contextual group which is most suitable for the recommendation of an item. In general, the selection of suitable contextual group in CARS is faced with challenges due to uncertainty in the classification of items with missing non-binary ratings. The underlying reaction of each user towards an item is implicitly assumed to be binary in nature because of which uncertainty occurs in the item classification. In particular, such a situation where ratings are missing for an item, make the exploitation of appropriate contextual group difficult for an item recommendation. In this article, we address the problem of the inappropriate classification of items into irrelevant contextual groups due to missing non-binary ratings. To this extent, we propose a three-way classification model using game-theoretic N-soft sets for improving the classification process by handling missing ratings. In particular, a game is formulated using game-theoretic N-soft sets to determine the effective threshold configuration used to induce three-way classification of items with missing non-binary ratings. Moreover, a thorough evaluation of our proposed model is carried out on the datasets of LDOS-CoMoDa and InCarMusic, where outcome signifies the effectiveness and performance of the proposed model.