Efficient optimization for L-extSKY recommendations

based on Regular Grid) za dobivanje L-extSKY objekata u jednom jedinom podprostoru. Međutim, u okruženju s više korisnika, sustav obično simultano rješava mnogostruke podprostorne L-extSKY preporuke. U ovom radu stoga predstavljamo učinkoviti algoritam AOMSR (Algorithm for Optimizing Multiple Subspace L-extSKY Recommendations) u svrhu značajnog smanjenja ukupnog vremena odziva. Nadalje, raspravljamo o dvije interesantne varijacije L-extSKY preporuke, tj. globalnom ograničenju L-extSKY preporuke i lokalnom ograničenju L-extSKY preporuke, koje su od praktičnog značaja i pokazuju kako se naš algoritam može primijeniti u svrhu njihove učinkovite obrade. Detaljna teoretska analiza i velik broj eksperimenata kojima se demonstrira naše rješenje su i efikasni i efektivni.L-extSKY recommendation has recently received a lot of attention in information retrieval community. Literature [1] proposes an algorithm EARG (Effi-cient Approach based on Regular Grid) to produce the L-extSKY objects in one single subspace. However, in multi-user environments, the system gener-ally handles multiple subspace L-extSKY recommendations simultaneously. Hence, in this paper, we present an efficient algorithm AOMSR (Algorithm for Optimizing Multiple Subspace L-extSKY Recommendations) to remarkably reduce the total response time. Furthermore, we discuss two interesting variations of L-extSKY recommendation, i.e., global constraint L-extSKY recommendation and local constraint L-extSKY recommendation, which are meaningful in practice, and show how our algorithm can be applied for their efficient processing. Detailed theoretical analyses and extensive experiments that demonstrate our solution are both efficient and effective

Efficient Computation of Subspace Skyline over Categorical Domains

Platforms such as AirBnB, Zillow, Yelp, and related sites have transformed the way we search for accommodation, restaurants, etc. The underlying datasets in such applications have numerous attributes that are mostly Boolean or Categorical. Discovering the skyline of such datasets over a subset of attributes would identify entries that stand out while enabling numerous applications. There are only a few algorithms designed to compute the skyline over categorical attributes, yet are applicable only when the number of attributes is small. In this paper, we place the problem of skyline discovery over categorical attributes into perspective and design efficient algorithms for two cases. (i) In the absence of indices, we propose two algorithms, ST-S and ST-P, that exploits the categorical characteristics of the datasets, organizing tuples in a tree data structure, supporting efficient dominance tests over the candidate set. (ii) We then consider the existence of widely used precomputed sorted lists. After discussing several approaches, and studying their limitations, we propose TA-SKY, a novel threshold style algorithm that utilizes sorted lists. Moreover, we further optimize TA-SKY and explore its progressive nature, making it suitable for applications with strict interactive requirements. In addition to the extensive theoretical analysis of the proposed algorithms, we conduct a comprehensive experimental evaluation of the combination of real (including the entire AirBnB data collection) and synthetic datasets to study the practicality of the proposed algorithms. The results showcase the superior performance of our techniques, outperforming applicable approaches by orders of magnitude