Bug: possible mistake in MMR calculation

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Hello,

If I am reading the code correctly, there is a mistake in the implementation of maximal marginal relevance (MMR) calculation.

Referring to the original publication https://doi.org/10.1145/290941.291025, the calculation is:


and the code as currently implemented:
```python
mmr = (
1 - diversity
) * candidate_similarities - diversity * target_similarities.reshape(-1, 1)
mmr_idx = candidates_idx[np.argmax(mmr)]
```

assuming:

- `diversity` is equal to 1-λ
- _Sim_1(D_i,Q)_ corresponds to `candidate similarities`
- _max Sim_2(D_i,D_j)_ corresponds to `target_similarities`

and I am assuming the last point because of the code:

```python
target_similarities = np.max(
word_similarity[candidates_idx][:, keywords_idx], axis=1
)
```

the code should be:

```python
mmr = (1 - diversity) *
(candidate_similarities - diversity * target_similarities.reshape(-1, 1))
mmr_idx = candidates_idx[np.argmax(mmr)]
```

So it appears to me that `diversity` is not distributed to both similarity terms as in the original equation; there needs to be parens around the difference between the similarity terms

I would note that I have seen a similar lack of parentheses, which distribute the diversity term (λ), in other works, for example http://www.cs.bilkent.edu.tr/~canf/CS533/hwSpring14/eightMinPresentations/handoutMMR.pdf

Locate the Python MMR implementation containing the shown `mmr` expression and trace how `candidate_similarities`, `target_similarities`, and `diversity` are computed. Compare the implementation with the cited publication and determine the intended λ mapping; done means the formula is resolved and its behavior is checked by the project's existing tests.

Written by the indexing model from the issue text.