# New Hybrid Techniques for Business Recommender Systems
## 9 Conclusions and Future Work  
In this work, we considered the application of recommender systems to business consultancy. We have argued how certain consultancy tasks can be formulated as recommendation problems, especially in the domain of IT consultancy, e.g., selection and orchestration of web services or selection of Key Performance Indicators and dimensions for BI solutions. Since such problems are in several respects different from the typical, purely preference-based B2C recommenders, we have addressed the question which (combinations of) recommendation techniques are most suitable for these new B2B scenarios. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Query Size** & **Graph-** & **CBR** & **Hybrid** & **Hybrid** & **Hybrid** & **Hybrid** \\ (**Verbosity**) & **Based** & **Top-2 Cases** & **\(\beta\) = 0.1** & **\(\beta\) = 0.3** & **\(\beta\) = 0.5** & **\(\beta\) = 0.9** \\ \hline
0 & 0.408 & 0.783 & 0.801 & 0.801 & 0.801 & 0.801 \\
5 & 0.566 & 0.805 & 0.836 & 0.835 & 0.836 & 0.835 \\
10 & 0.646 & 0.805 & 0.843 & 0.845 & 0.839 & 0.840 \\
15 & 0.689 & 0.807 & 0.804 & 0.856 & 0.849 & 0.849 \\
20 & 0.713 & 0.807 & 0.817 & 0.862 & 0.853 & 0.851 \\
30 & 0.733 & 0.812 & 0.821 & 0.864 & 0.856 & 0.852 \\
40 & 0.742 & 0.812 & 0.825 & 0.866 & 0.858 & 0.854 \\
100 & 0.746 & 0.812 & 0.827 & 0.866 & 0.858 & 0.854 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiment 2: MAP values for individual recommendation techniques and hybrid strategy. We worked with data from the BI consultancy domain and performed experiments with a range of known recommender techniques. These techniques offer a varying degree of possibility to feed--besides the item choices that a company makes--contextual knowledge, such as company demographics, into the algorithm. This ranges from none (collaborative filtering) over limited (graph-based random walks) to full coverage (CBR-based recommender). Our initial comparison showed that--as one might expect--the CBR-based recommendation benefits from its ability to accommodate more contextual knowledge and provides the best results. However, we also recognised a limitation: CBR-based recommenders have a free parameter, namely \(n\), the number of most similar cases to use for the identification of possible solution elements. We found that, for the rather small case base in our experiments, small values of \(n\) performed better. Obviously, a larger \(n\) implies more noise coming from more dissimilar cases. In our previous work [3], we already observed that including cases from different but similar industries can be dangerous. On the other hand, limiting \(n\) also limits the potential recall of the recommender, i.e., some useful items from less similar cases are excluded. Obviously, a graph-based approach--although less precise--offers a natural way to include more items from the more dissimilar cases. We, therefore, explored the combination of CBR-based recommendation with a graph-based recommender in order to combine its strengths in terms of precision with the graph-based recommender's strength in providing more relevant items in the lower ranks. We followed a weighted hybridisation strategy. The weight was dynamic, giving more and more importance to the graph recommender with the growing size of the query. This makes sense since contextual knowledge becomes less important as we know more and more about already chosen items. Because of the superior performance of the CBR recommender, we also designed the weighting so as to ensure that there is always a certain minimum weight given to it. It turned out that, indeed, this minimum weight should not be 0. We found that the weighted hybrid performed better than any of the individual recommenders at all levels of query verbosity.