Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations

Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity - i.e., the item similarity evidenced by user interactions like ratings and purchases. Nevertheless, there exist multiple relations between items in real-world scenarios, e.g., two movies share the same director, two products complement with each other, etc. Distinct from the collaborative similarity that implies co-interact patterns from the user's perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF) to exploit multiple item relations in recommender systems. We find that both the relation type (e.g., shared director) and the relation value (e.g., Steven Spielberg) are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference - the first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with user preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF1. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by RCF's modeling of multiple item relations

Biogas upgrading by CO₂ removal with a highly selective natural amino acid salt in gas–liquid membrane contactor

For biogas upgrading, a natural amino acid salt, potassium l-argininate (PA) is studied in a membrane contactor to capture CO₂ from biogas. CO₂ removal performance in terms of the overall volumetric gas phase mass transfer coefficient, membrane selectivity towards CO₂ and the economic cost factor is systematically investigated. It is shown that PA is a highly CO₂ selective absorbent and has a better affinity towards CO₂ than monoethanolamine (MEA). The highest CH₄ content in the upgraded biogas can reach about 99.15 vol% by using PA, fully meeting the requirement of biogas upgrading. Furthermore, lower solvent concentration, lower liquid velocity and higher reaction temperature may be adopted when using PA in comparison to MEA. PA also has a better flexibility to the change of CO₂ partial pressure and biogas flow rate than MEA. Regarding the economic cost factor of membrane process, CO₂ loading of the lean PA solution can be optimized to 0.69–0.78 mol/mol as the suitable range. Moreover, CO₂ removal performance of l-arginine (ARG) is also explored. Due to the large amounts of bicarbonate other than carbamate formed in CO₂-rich ARG solution, ARG has a lower biogas upgrading capability than diethanolamine (DEA) but higher than triethanolamine (TEA).11 page(s

Ecotourism industry in constrained environments: Bhutan as a case study

This chapter analyses the current situation of tourism in Bhutan and discusses the opportunities and challenges of ecotourism in this country. It combines Bhutan’s tourist data, the current tourism policy of the Bhutanese government and the government's measures for culture and environmental protection, exploring the potential development methods to improve and maintain the sustainability of Bhutan's natural and cultural environment by enhancing its ecotourism industry. Furthermore, it also enumerates the adverse effects of over-exploration of tourism in other cities such as Nepal with respect to the importance of ecotourism. A multilayered qualitative research methodology was conducted to determine whether citizens and visitors are in favour of ecotourism activities that might benefit local communities and cultures. It can be concluded that the development of ecotourism is conducive to improving and maintaining the sustainability of Bhutan's natural and cultural environment. Finally, this research ends by providing key recommendations to promote the development of ecotourism to protect Bhutanese people and the country’s Gross National Happiness

VALORIZATION OF BIOGAS THROUGH SIMULTANEOUS CO<inf>2</inf> AND H<inf>2</inf>S REMOVAL BY RENEWABLE AQUEOUS AMMONIA SOLUTION IN MEMBRANE CONTACTOR

● Simultaneous H2S and CO2 removal from biogas is studied.● Renewable absorbent from biogas slurry is used in membrane contactor.● More than 98% of H2S can be removed by membrane absorption.● The impurities have less influence on H2S removal efficiency. Upgrading biogas into biomethane not only improves the biogas utilization as vehicle fuel or natural gas substitute, but also reduces the greenhouse gases emissions. Considering the principle of engineering green energy process, the renewable aqueous ammonia (RAA) solution obtained from biogas slurry was used to remove H2S and CO2 simultaneously in the hollow fiber membrane contactor. RAA was mimicked in this study using the ammonia aqueous solution mixed with some typical impurities including ethanol, acetic acid, propionic acid, butyric acid and NH4HCO3. Compared with the typical physical absorption (i.e., pure water) removing 48% of H2S from biogas, RAA with 0.1 mol&#183;L&#8722;1 NH3 could remove 97% of H2S. Increasing the NH3 concentration from 0.1 to 0.5 mol&#183;L&#8722;1 could elevate the CO2 absorption flux from 0.97 to 1.72 mol&#183;m&#8722;2&#183;h&#8722;1 by 77.3%. Among the impurities contained in RAA, ethanol has a less impact on CO2 absorption, while other impurities like CO2 and acetic acid have significant negative impacts on CO2 absorption. Fortunately, the impurities have a less influence on H2S removal efficiency, with more than 98% of H2S could be removed by RAA. Also, the influences of operating parameters on acid gases removal were investigated to provide some engineering suggestions

Once-through CO2 absorption for simultaneous biogas upgrading and fertilizer production

&copy; 2017 Elsevier B.V. A new process is developed for biogas upgrading using the total ammonia nitrogen (TAN) in biogas slurry as a renewable absorbent. TAN in biogas slurry can be transferred into free ammonia by adding NaOH to increase the solution pH. Increasing the pH of biogas slurry to 10 causes that &gt;&nbsp;90% TAN transfers into free ammonia, leading to high TAN removal ratios. However, further increasing the pH of biogas slurry has limited effects. Vacuum membrane distillation (VMD) has higher kinetics constants and thus is a more effective way to recover and enrich ammonia from biogas slurry compared with thermal or air stripping. After VMD, the recovered aqueous ammonia solution with high TAN concentrations and the enhanced biogas slurry can be used as &ldquo;once-through&rdquo; CO2 absorbents. With alkaline addition, VMD does not increase the CO2 absorption capacity, but significantly minimizes the phytotoxicity of biogas slurry. When NaOH dosage is below 0.25&nbsp;M, superior ammonia separation performance with high kinetics constants and low phytotoxicity can be achieved. The recovered aqueous ammonia solution also has excellent CO2 absorption performance for biogas upgrading and can help obtain high content of methane. This study provides an effective process for biogas upgrading with low costs and generation of valuable products, including high purity bio-methane, low phytotoxicity biogas slurry for agricultural application and high concentration NH4HCO3 as a fertilizer

CO₂ removal from biogas by using green amino acid salts : performance evaluation

Five natural amino acid salts (AASs) as green absorbents for CO₂ removal from biogas are evaluated using the typical absorption–regeneration screening method in the present study. CO₂ absorption performance and reaction mechanism of L-arginine are also investigated. Experimental results show that the initial CO₂ absorption rate increases but the regeneration efficiency decreases with the rise in the basicity of AASs. Potassium L-ornithinate and potassium glycinate have some overwhelming advantages such as negligible absorbent loss, high absorption kinetics, relatively low absorption enthalpy, and high regeneration efficiency, making them suitable and favorable candidates for CO₂ absorption from biogas. L-arginine may be superior to monoethanolamine in terms of the saturated CO₂ absorption loading, absorption enthalpy and regeneration efficiency, but it suffers from slow reaction kinetics. The results of FTIR analysis suggest that L-arginine is more likely to act as a base in catalyzing the hydration of CO₂. Both the cyclic CO₂ uptake and the molecular weight of the absorbent should be considered in absorbent screening. Adopting AASs with high cyclic CO₂ uptakes may not be effective in minimizing the absorber/desorber size due to their high molecular weights.10 page(s