Learning users' interests by quality classification in market-based recommender systems

Recommender systems are widely used to cope with the problem of information overload and, to date, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents who supplied them according to the users’ ratings of their suggestions. Moreover, we have theoretically shown how our system incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively in practice, however, each agent needs to be able to classify its recommendations into different internal quality levels, learn the users’ interests for these different levels, and then adapt its bidding behaviour for the various levels accordingly. To this end, in this paper we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy does indeed help the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations

Flavor-twisted boundary condition for simulations of quantum many-body systems

We present an approximative simulation method for quantum many-body systems based on coarse graining the space of the momentum transferred between interacting particles, which leads to effective Hamiltonians of reduced size with the flavor-twisted boundary condition. A rapid, accurate, and fast convergent computation of the ground-state energy is demonstrated on the spin-1/2 quantum antiferromagnet of any dimension by employing only two sites. The method is expected to be useful for future simulations and quick estimates on other strongly correlated systems.Comment: 6 pages, 2 figure

Luby Transform Coding Aided Iterative Detection for Downlink SDMA Systems

A Luby Transform (LT) coded downlink Spatial Division Multiple Access (SDMA) system using iterative detection is proposed, which invokes a low-complexity near-Maximum-Likelihood (ML) Sphere Decoder (SD). The Ethernet-based Internet section of the transmission chain inflicts random packet erasures, which is modelled by the Binary Erasure Channel (BEC), which the wireless downlink imposes both fading and noise. A novel log-Likelihood Ratio based packet reliability metric is used for identifying the channel-decoded packets, which are likely to be error-infested. Packets having residual errors must not be passed on to the KT decoder for the sake of avoiding LT-decoding –induced error propagation. The proposed scheme is capable of maintaining an infinitesimally low packet error ratio in the downlink of the wireless Internet for Eb/n0 values in excess of about 3dB