Investigating the impact of recommender systems on user-based and item-based popularity bias

Abstract Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular. In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score. The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages

Unlucky explorer:a complete non-overlapping map exploration

Abstract In this work, we introduce the Maze Dash puzzle as an exploration problem where the agent must find a Hamiltonian Path visiting all the cells with a minimum number of turnings for most cases. We also discuss the real-world application of the problem, such as 8 ball billiards and Snooker games. We investigate different methods by a focus on Monte-Carlo Tree Search (MCTS) and SAT to get an overview of which class of solutions solves the puzzle quickly and accurately. Also, we perform optimization to the proposed MCTS algorithm to prune the tree search. Also, since the prefabricated test cases of this puzzle are not large enough to assay the proposed method, we employ a technique to generate solvable test cases to evaluate the approaches. Eventually, our comparison indicates that the MCTS-based approach is an up-and-coming method that could cope with the test cases with small and medium sizes with faster run-time than SAT. However, for specific discussed reasons, including the features of the problem, tree search organization, and also the approach of MCTS in the Simulation step, MCTS takes more time to execute in large size scenarios. Our results can be employed to choose a proper approach to create an AI to solve the Maze Dash, 8 ball billiards, and Snooker games

A study on non-overlapping multi-agent pathfinding

Abstract In this work, first, we model the non-overlapping Multi-Agent Pathfinding (MAPF) to an NP-complete traditional puzzle called Numberlink puzzle owing to its features. Interestingly, this puzzle is reasonably shown to be analogous to the Flow Free game. Hence an approach that solves the puzzle can be considered as the AI for solving Flow Free game. Then, we investigate various promising approaches such as SAT, Heuristics, and Monte-Carlo Tree Search (MCTS) based methods to find a fast and accurate solution and provide a fair comparison. We implement and evaluate two SAT and MCTS-based approaches. Finally, we propose an enhanced MCTS with three optimizations to solve the problem faster with lower memory consumption, particularly in significant test sizes with many agents. All the methods are compared and analyzed on the same test cases in different grid sizes and various agents. The optimized MCTS-based method solves the most extensive test case with a size of 40 × 40 with 100 agents in 988.5 s, respectively, indicating 22.8% and 63.6% improvements in time and memory consumption compared to the state-of-the-art MCTS-based method. It also shows 72% and 39.2% improvement in performance with lower memory consumption than the best results of investigated SAT and heuristic-based methods, sequentially

On using Monte-Carlo tree search to solve puzzles

Abstract Solving puzzles has become increasingly important in artificial intelligence research since the solutions could be directly applied to real-world or general problems such as pathfinding, path planning, and exploration problems. Selecting the best approach to solve puzzles has always been an essential issue. Monte-Carlo Tree Search (MCTS) has surged into popularity as a promising approach due to its low run-time and memory complexity. Thus, it is required to know how to employ this method to solve the puzzles. In this work, we study the applicability of MCTS in solving puzzles or solving a puzzle with MCTS, not comparing many MCTS approaches. We propose a general classification of puzzles based on their features. This classification consists of four primary classes that provide a mathematical formula for each and their satisfactory criteria. This classification let us know how to utilize MCTS based on the puzzle’s features. We pass each puzzle to an MCTS algorithm as a series of satisfaction functions based on this mathematical formulation. The classification can perform general pathfinding or path-planning if the outlining problem is defined within the described mathematical constraints. MCTS progressively solves a puzzle until the functions are completely satisfied in our proposed classification. We examine different puzzles for each class using our proposed methodology. Furthermore, to evaluate the proposed method’s performance, each of these puzzles is compared with their available SAT solvers using the Z3 implementation and different variations of MCTS that are generally used