Toward a Knowledge-based Personalised Recommender System for Mobile App Development

Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users’ query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user’s query with the minimum mismatches.

"A Review of collaborative filtering Recommendation System

"Recommended systems, also known as systems of recommendation, are a part of information filtration systems which are utilized to predict the user’s estimation or choice for an object. In recent years, recommended systems have been extensively used in e-commerce programs. Music, news, books, research papers, and goods are likely to be the most popular E-commerce pages. This article provides an analysis of the scope of recommendation systems and discusses recommended systems that include Collaborative filtering (CF), one of the farthest common recommended methods, which are typically divided into three major categories: Approaches to recommendation that are content-based, collective, or hybrid."

DYNAMIC MEAN–VARIANCE OPTIMIZATION PROBLEMS WITH DETERMINISTIC INFORMATION

We solve the problems of mean–variance hedging (MVH) and mean–variance portfolio selection (MVPS) under restricted information. We work in a setting where the underlying price process [Formula: see text] is a semimartingale, but not adapted to the filtration [Formula: see text] which models the information available for constructing trading strategies. We choose as [Formula: see text] the zero-information filtration and assume that [Formula: see text] is a time-dependent affine transformation of a square-integrable martingale. This class of processes includes in particular arithmetic and exponential Lévy models with suitable integrability. We give explicit solutions to the MVH and MVPS problems in this setting, and we show for the Lévy case how they can be expressed in terms of the Lévy triplet. Explicit formulas are obtained for hedging European call options in the Bachelier and Black–Scholes models.

Application of Improved Recommendation System Based on Spark Platform in Big Data Analysis

In the era of big data, people have to face information filtration problem. For those cases when users do not or cannot express their demands clearly, recommender system can analyse user’s information more proactive and intelligent to filter out something users want. This property makes recommender system play a very important role in the field of e-commerce, social network and so on. The collaborative filtering recommendation algorithm based on Alternating Least Squares (ALS) is one of common algorithms using matrix factorization technique of recommendation system. In this paper, we design the parallel implementation process of the recommendation algorithm based on Spark platform and the related technology research of recommendation systems. Because of the shortcomings of the recommendation algorithm based on ALS model, a new loss function is designed. Before the model is trained, the similarity information of users and items is fused. The experimental results show that the performance of the proposed algorithm is better than that of algorithm based on ALS.

Involving Patients with E-health – The Dialogic Dynamics of Information Filtration Work

With e-health technologies, patients are invited as co-producers of data and information. The invitation sparks new expectations, yet often results in disappointments. With persistent ambitions to involve patients by means of e-health, it seems crucial to gain a better understanding of the nature, sources and workings of the expectations that come with being invited. I analyse the use of an e-health system for ICD-patients, focusing on how patients sought to serve as information providers. Continuing STS-research on invisible work in technology use, I show how using the system involved complex work of filtering information. I argue that this ‘filtration work’ was inherently dialogic, that is, characterized by receiver-orientation and the anticipation of response and guided by different communicative projects. For the patients, filtration work thus, first of all, required certain skills and knowledge about the infrastructure of care. Secondly, it entailed the expectation that the system?" for better or for worse?"would facilitate not just information sharing but open up a dialogue, which glaringly contrasted with the clinicians’ expectations of being able to better manage dialogue. I suggest that understanding the dialogic dynamics and ‘overflows’ of information filtration work can help unpack the challenges of facilitating (patient) participation with e-health and other filtration devices.

Convex duality in constrained mean-variance portfolio optimization

We apply conjugate duality to establish the existence of optimal portfolios in an asset-allocation problem, with the goal of minimizing the variance of the final wealth which results from trading over a fixed, finite horizon in a continuous-time, complete market, subject to the constraints that the expected final wealth equal a specified target value and the portfolio of the investor (defined by the dollar amount invested in each stock) take values in a given closed, convex set. The asset prices are modelled by Itô processes, for which the market parameters are random processes adapted to the information filtration available to the investor. We synthesize a dual optimization problem and establish a set of optimality relations, similar to the Euler-Lagrange and transversality relations of calculus of variations, giving necessary and sufficient conditions for the given optimization problem and its dual to each have a solution, with zero duality gap. We then solve these relations, to establish the existence of an optimal portfolio.

Convex duality in constrained mean-variance portfolio optimization

We apply conjugate duality to establish the existence of optimal portfolios in an asset-allocation problem, with the goal of minimizing the variance of the final wealth which results from trading over a fixed, finite horizon in a continuous-time, complete market, subject to the constraints that the expected final wealth equal a specified target value and the portfolio of the investor (defined by the dollar amount invested in each stock) take values in a given closed, convex set. The asset prices are modelled by Itô processes, for which the market parameters are random processes adapted to the information filtration available to the investor. We synthesize a dual optimization problem and establish a set of optimality relations, similar to the Euler-Lagrange and transversality relations of calculus of variations, giving necessary and sufficient conditions for the given optimization problem and its dual to each have a solution, with zero duality gap. We then solve these relations, to establish the existence of an optimal portfolio.